Analytica Docs - User contributions [en]

Analytica Docs

2012-02-15T19:43:54Z

Nkretz: Undo revision 21558 by Nkretz (talk)

This Analytica wiki offers tips, FAQs, videos, examples, shared libraries, and detailed function documentation that go way beyond the Analytica Tutorial and User Guides. It is a resource created by and for the community of Analytica users.

Every Analytica user with current support, has access to this wiki. If you didn't get a password, please email info@lumina.com to get one. Because it's a wiki, like Wikipedia, you can add your own contributions, as well as see others.

If you can't find the answer to your question or have a general comment, add [[Comments and suggestions for Analytica wiki]]. If you have a comment or question specific to an existing page, you can add it on its ''discussion'' page. (Each page has a Discussion tab at the top.) Or, if you know a good answer to someone's question, please provide it. Your contributions are welcome, whether questions, corrections, tips, examples, or libraries you want to share. As with any wiki, all contributions are liable to be edited and improved by others.

{| border="1" align="center"
|-
|
{| border="0" style="text-align: center;"
|-
| ''12 Jan 2012''
|-
| '''Analytica 4.4 Released'''
Announcing the release of Analytica 4.4 (and ADE 4.4) 
See [[Analytica 4.4|What's new]] or [http://www.lumina.com/support/downloads/ Download Installers]
|-
| <hr />
|-
| ''15-Feb-2012''
|-
| '''[http://blog.lumina.com Lumina Blog] is ''live''!'''
|}
|}

*[[Analytica User FAQs]]: Frequently Asked Questions and support queries.
*[[Analytica Libraries and Templates]] and [[Example Models]]:
*[[Analytica Modelers Guide]]: Hints and examples.

*[[Analytica Reference]]: Complete details on selected features.
**[[:Category:Functions|Function Reference Index]]
**[[:Category:Concepts|Concepts]]

*[[Analytica in the Classroom]]: Resources for using Analytica for teaching

*[[Analytica User Group]] : Recorded videos of past webinars -- a great resource for learning key topics, basic and advanced.
**[[Analytica User Group/Past Topics|Archives of previous webinars]]

*[[What's new in Analytica 4.4?]]: Released 12 January 2012, the page lists enhancements since the 4.3 release.

*[[Analytica Cloud Player]]: Share your models over the web.

* [http://blog.lumina.com The Lumina Blog]

*[[Enhancement Requests for Analytica]]: Here's what else I think Analytica should do.

*[[Comments and suggestions for Analytica wiki]]: Here's how we can improve this Wiki.

*[[Job Postings]]: Jobs available for Analytica users.

*[[How to edit Analytica wiki]]: Tips on how to edit and add to these documents.

*List of [[Special:Uncategorizedcategories|Top Level Categories]] or [[Special:Categories|All Categories]] (indexes into the Analytica Wiki site)

[[Image:Analytica logo tagline 500px.png]]

Analytica Docs

2012-02-11T02:12:28Z

Nkretz:

This Analytica wiki offers tips, FAQs, videos, examples, shared libraries, and detailed function documentation that go way beyond the Analytica Tutorial and User Guides. It is a resource created by and for the community of Analytica users.

Every Analytica user with current support, has access to this wiki. If you didn't get a password, please email info@lumina.com to get one. Because it's a wiki, like Wikipedia, you can add your own contributions, as well as see others.

If you can't find the answer to your question or have a general comment, add [[Comments and suggestions for Analytica wiki]]. If you have a comment or question specific to an existing page, you can add it on its ''discussion'' page. (Each page has a Discussion tab at the top.) Or, if you know a good answer to someone's question, please provide it. Your contributions are welcome, whether questions, corrections, tips, examples, or libraries you want to share. As with any wiki, all contributions are liable to be edited and improved by others.

{| border="1" align="center"
|-
|
{| border="0" style="text-align: center;"
|-
| ''12 Jan 2012''
|-
| '''Analytica 4.4 Released'''
Announcing the release of Analytica 4.4 (and ADE 4.4) 
See [[Analytica 4.4|What's new]] or [http://www.lumina.com/support/downloads/ Download Installers]
|-
| <hr />
|}
|}

*[[Analytica User FAQs]]: Frequently Asked Questions and support queries.
*[[Analytica Libraries and Templates]] and [[Example Models]]:
*[[Analytica Modelers Guide]]: Hints and examples.

*[[Analytica Reference]]: Complete details on selected features.
**[[:Category:Functions|Function Reference Index]]
**[[:Category:Concepts|Concepts]]

*[[Analytica in the Classroom]]: Resources for using Analytica for teaching

*[[Analytica User Group]] : Recorded videos of past webinars -- a great resource for learning key topics, basic and advanced.
**[[Analytica User Group/Past Topics|Archives of previous webinars]]

*[[What's new in Analytica 4.4?]]: Released 12 January 2012, the page lists enhancements since the 4.3 release.

*[[Analytica Cloud Player]]: Share your models over the web.

* [http://blog.lumina.com The Lumina Blog]

*[[Enhancement Requests for Analytica]]: Here's what else I think Analytica should do.

*[[Comments and suggestions for Analytica wiki]]: Here's how we can improve this Wiki.

*[[Job Postings]]: Jobs available for Analytica users.

*[[How to edit Analytica wiki]]: Tips on how to edit and add to these documents.

*List of [[Special:Uncategorizedcategories|Top Level Categories]] or [[Special:Categories|All Categories]] (indexes into the Analytica Wiki site)

[[Image:Analytica logo tagline 500px.png]]

Analytica User Group

2011-04-10T15:04:26Z

Nkretz: /* Optimization with Uncertainty */

[[/Past Topics|Past Topics]] >>

The Analytica User Group is a way for Analytica users to support each other by sharing tips and function libraries. It includes a webinar series with tutorials and demonstrations on key Analytica features.

= Webinar Series =

Analytica User Webinars are a great way to learn about key Analytica features and modeling skills. They are live demos, using Citrix Gotowebinar, and VOIP or your telephone, at your choice. Questions, comments, and tangents are welcome.

There are webinars most weeks, usually on Thursdays at 10am PST (1pm EST). Seats are limited. To sign up for a particular webinar, see "How to Attend" below. Presentations may last anywhere from 20 to 90 minutes (with an estimate provided upfront).

If you missed a User Webinar, don't despair. We usually records them, including audio and screensharing. So, you can replay them any time. See below for the full list.

;Recent talks include:
* [[/Past_Topics#Expecting_the_Unexpected:_Coping_with_surprises_in_Probabilistic_and_Scenario_Forecasting|Expecting the Unexpected]] (07 April 2011)
* [[/Past_Topics#Optimizing_Parameters_in_a_Complex_Model_to_Match_Historical_Data|Optimizing Parameters in a Complex Model to Match Historical Data]] (31 Mar 2011)
* [[/Past_Topics#Interactive Optimization Workshop|Interactive Optimization Workshop]] (24 Mar 2011)
* [[/Past_Topics#Introduction_to_Structured_Optimization|Introduction to Structured Optimization]] (24 Feb 2011)

== Schedule of Upcoming Webinars ==

=== Optimization with Uncertainty ===

'''Date and Time:''' Thursday, 14 April 2011, 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Ph.D., Lumina Decision Systems

'''Register at''' [https://www2.gotomeeting.com/ojoin/657053954/182596]

'''Abstract'''

Analytica analyzes uncertainty by conducting a Monte Carlo analysis. When you optimize decision variables in a model containing uncertainty, you have a choice: You can perform one optimization over the Monte Carlo analysis, or you can perform a Monte Carlo sampling of optimizations (i.e., the Monte Carlo is inside the optimization, or the optimization is inside the Monte Carlo). The first case is used when the decision must be taken while the quantities are still uncertain. The second case is used when the values of the uncertain quantities will be resolved before the decisions are taken.

To illustrate, consider the situation faced by a relief organization that provides aid to victims of large natural disasters. In one situation, a decision must be made regarding how many resources to deploy to one particular location that has been hit by a large tsunami. At the time the decision must be made, the number of casualties is highly uncertain. In a different situation, the organization wants to characterize the uncertainty in its need for resources, given that it will optimally deploy resources in response to natural disasters as they occur.

=== Neural Networks ===

'''Date and Time:''' Thursday, 21 April 2011, 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Ph.D.

'''Abstract'''

A feed-forward artificial neural networks is a non-linear function that predicts one or more outputs from a set of inputs. These are usually used in two layers, where the first layer of inputs are weighted and summed, and then passed through a sigmoid function to determine the activations of a hidden layer, those those activations are weighted, summed and then passed through a sigmoid function to predict the final output. A training phase is used to adjust the weight to "fit" an example data set.

In this webinar, I'll create a nearal network model in Analytica and train it on example data as a demonstration of the use of structured optimization. It provides a simple and easily understood example of the use of intrinsic indexes in a structured optimization model, while at the same time introducing the basics of the interesting topic if neural networks.

=== Linearizing Optimization Models ===

=== [[Model building by mouse]] ===

'''Date and Time:''' (TBD)

'''Presenter''': (TBD)

'''Abstract'''

See Model building by mouse for the concept. The talk will also cover the TemplateInput and TemplateOutput attributes first introduced in 4.2 that make template modules possible, and thus expand the potential power and applicability of this modeling technique.

=== Saving Memory: Controlling when results are (and are not) cached ===

'''Date and Time:''' (TBD)

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

When a variable is computed, Analytica caches the result (i.e., keeps a copy around) so that when the same result is needed later, it does not need to be recomputed. Large array-valued results can end up consuming large amounts of memory, and lead to annoyances such as running out of available memory. There are instances where it isn't really necessary to cache a result -- either it will never be needed a second time, or it isn't a big deal to recompute it, or it is an exact copy of its parent (perhaps because only the result graph settings are different), so that by configuring that variable to not cache its result, or to release its cache as soon as all its children are computed, memory space can be recovered, perhaps allowing larger simulations to be conducted.

Analytica 4.2 introduces a new feature allowing you to configure when results are (or are not) cached. I'll demonstrate how to utilize this feature, and discuss limitations, such as situations where it would be a very bad idea not to cache.

If time allows (it probably will), I'll also throw in two other small memory-related controls new in 4.2. The [[WhatIf]]-style functions ([[WhatIf]], [[WhatIfAll]], [[Dydx]], [[Elasticity]], [[NlpDefine]]), i.e., functions that compute hypotheticals, have been modified in 4.2 to preserve previously computed values of other variables. This is a nice feature, but can impact tight-memory cases. I'll explain this change and how to avoid the extra memory consumption where appropriate. Second, I'll also show how Analytica's maximum working set size can be configured (on some operating systems), which can be used to keep other applications on your computer highly responsive even when large memory-intensive computations are churning away.

In Analytica 64-bit, you are less likely to encounter the problem of running out of memory, but you still may encounter performance slowdown from huge memory utilization. Some large models, requiring extensive memory resources, run smoothly even when requiring far more memory than there is RAM available, while other result in extensive ''thrashing'', taking very long to evaluate. I've found that differentiating factor has to do with the size of individual arrays within the model. Thrashing tends to occur when a single array consumes more than about 1/3 the available RAM, while if all arrays in the model remain small relative to available RAM, huge memory evaluations often run very smoothly. I'll relate these observations, and what that means with respect to sample size.

This talk is appropriate for advanced Analytica modelers.

=== The [[Dynamic]] Function and Dynamic Loops ===

'''Date and Time:''' (TBD)

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

The [[Dynamic]] function in Analytica 4.2 is a bit more flexible than previously. It is now possible for [[User-Defined Functions]] to be part of a dynamic loop, and there are cases where it is now possible to operate over the [[Time]] index where previous it was not. I'll discuss how dynamic models are evaluated, and how that differs from non-dynamic models, and cover some tricks for tracing evaluation and debugging complex dynamic models (which can often be challenging to debug). I'll also introduce how [[Dynamic]] can be used on an index other than the built-in [[Time]] index.

=== [[IntraTable]]s ===

'''Date and Time:''' (TBD)

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

An [[IntraTable]] is a variation of an edit [[Table]], where (like [[Table]]s), the cells may contain expressions, but unlike [[Table]]s, the expressions in each cell can refer to values of other cells. This may (quite appropriately) conjure up nightmares of spreadsheet hell. While this isn't something you'd want to use nonchalantly, there are cases when the ability to reference other cells within the same table comes with its benefits. Because the [[IntraTable]] allows any pattern of references (it doesn't have to be strictly left-to-right, for example), as long as a cell-cycle isn't created, it is possible to encode certain recurrences that would otherwise be quite difficult to encode.

== How to Attend ==

To attend, you need to sign up by contacting Lumina at mailto:webinars@lumina.com. Please sign up at least a day prior. Attendance is limited, so please don't sign up unless you sincerely intend to attend.

These Webinars are FREE to users who have an up-to-date Support for Analytica. If you are unsure, check with mailto:sales@lumina. For those without current support, the fee is US$50.

== How to be a Presenter ==

Being a presenter at an Analytica webinar provides an opportunity to make others in the Analytica community aware of your successes or capabilities. Consultants may find this an opportunity for exposure to others with particular modeling needs. Also, if you are an Analytica aficionado, this is a great opportunity to help others.

If you would like to be a presenter, submit your proposed topic to webinars@lumina.com and possible presentation times (include the time zone). We will schedule the GotoMeeting conference (you do not need a gotoMeeting subscription yourself) and we will make you presenter during the session, allowing you to share your screen while you talk. You will most likely make use of Power Point and a running Analytica during your presentation.

==Analytica User Group:([[Analytica_User_Group/Past_Topics#The_Large_Sample_Library|Large Sample Library]])==

== [[Analytica User Group/Past Topics|Archive of Past Topics]] ==

Visit the [[Analytica User Group/Past Topics|Archive of Past Topics]] for abstracts, example models, and recordings of previous Analytica User Group webinars. Previous topics that have been presented include:

* Guidelines for Model Transparency
* Automated Monitoring and Failure Detection
* Principal Component Analysis (PCA)
* Internal Rate of Return ([[IRR]]) and Modified Internal Rate of Return ([[MIRR]])
* Bond Portfolio Analysis
* Net Present Value ([[NPV]])
* The Analytica Wiki, and How to Contribute
* Data Center Capacity Planning
* Modeling the Precision Strike Process
* The Performance Profiler
* Variable Stiffness Cubic Splines
* Introduction to [[DetermTable]]s
* Importance Sampling (Bayesian posteriors)
* Importance Sampling (rare events)
* Statistical Functions
* [[Table Splicing]]
* Analytica Web Player
* [[SubTable|SubTables]]
* Creating Custom Distribution Functions
* Using the Check attribute to validate inputs and results
* The [[Dynamic]] Function
* [[Logistic Regression]]
* Creating Control Panels
* Statistical Functions
* Creating Scatter Plots
* Using [[Regression]]
* New Functions for Reading Directly from an Excel File
* Advanced Tornado Charts -- when inputs are Array-Valued
* Using the Analytica Decision Engine (ADE) from ASP.NET
* OLE Linking
* Tornado Charts
* Correlated and Multivariate Distributions
* Assessment of Probability Distributions
* Graph Style Templates
* Sneak preview of Analytica Web Publisher
* Querying an OLAP server
* Querying an ODBC relational database
* [[Self-Indexed Arrays|Self-Indexes]], Lists and [[Implicit Dimensions]]
* Flattening and Unflattening of Arrays
* Introduction to Arrays and Array Abstraction
* [[Local Indexes]]
* The [[Iterate]] Function
* The [[Using_References|Reference and Dereference Operators]]
* Modeling Utility Tariffs in Analytica
* Modeling Energy Efficiency in Large Data Centers
* Calling External Applications
* Introduction to Linear and Quadratic Programming
* Non-Linear Optimization
* Writing [[User-Defined Functions]]
* Modeling Markov Processes in Analytica
* Manipulating Dates in Analytica
* Button Scripting
* Manipulating Indexes and Arrays in Analytica Expressions
* Edit Table Enhancements in Analytica 4.0
* [[Handle]]s and [[Meta-Inference]]

== Potential future topics ==

If you would like to see a webinar on a given topic, please feel free to add it here. If you see a topic listed and would like to be a presenter, let us know.

* Statistical hypothesis testing.
** Standard textbook tests
** Computing p-values using Monte Carlo for complex and non-standard statistical models.

* Time-series analysis

* Using the interpolation functions, [[LinearInterp]], [[CubicInterp]], [[StepInterp]].

* Numeric precision - numeric round-off, underflow, etc., why they happen, what to do about it.

* Net Present Value and Internal Rate of Return -- introduction to the use of these commonly used metrics for quantifying decision quality.

* Using the ''Performance Profiler'' to understand where your model consumes time and memory resources.

* Producing graphs from ADE: Including how to serve graphs from web pages.

* Intracacies of the Domain attribute

* Mastering Array Abstraction

* Writing User-Defined Distribution functions.

* Getting data into Analytica.

* [[DetermTable]]s

* Large-scale sampling. Techniques when memory limitations constrain sampleSize.

* Creating User-Defined functions. Review of [[Function Parameter Qualifiers]].

* Understanding [[Evaluation Contexts]]

* Bayesian Inference

* Sensitivity Analysis.

* Dynamic Programming

* Approximate and Stochastic Dynamic Programming

== User Survey Results ==

During the first week of September, we sent out a survey to people who had attended webinars so far. Please continue providing us with feedback. Here is some feedback to date:

Level of difficult and speed:

So far, of those who answered this question, 25% say "a bit too easy", 25% say "too hard/fast", and 50% say "just right".

Topics requested for future webinars:

* How to run multiple iterations, e.g. 100 iterations with uncertainty sample of 1000.

* Sampling for rare events.

* Using the lognormal function.

* Financial modeling

* New array functionality (subtables, choices in tables)

* Optimizer !!!

* Dynamic models (twice requested)

* Choice of distributions

* Re-sampling and radomize methods and uncertainty sample size

* Tricks for sensitivity analysis

* Input and output nodes

* Importance analysis

= The Analytica Wiki =

The [[Analytica Wiki ]] contains many resources, including in-depth reference materials, relevant articles, example models, tutorials, etc., to help users master Analytica and find what they need. Even better, Analytica users can contribute! You can upload your own models, articles, expand on or correct materials that are there, etc., for the benefit of the entire Analytica community.

Analytica User Group

2011-04-10T15:04:07Z

Nkretz: /* Optimization with Uncertainty */

[[/Past Topics|Past Topics]] >>

The Analytica User Group is a way for Analytica users to support each other by sharing tips and function libraries. It includes a webinar series with tutorials and demonstrations on key Analytica features.

= Webinar Series =

Analytica User Webinars are a great way to learn about key Analytica features and modeling skills. They are live demos, using Citrix Gotowebinar, and VOIP or your telephone, at your choice. Questions, comments, and tangents are welcome.

There are webinars most weeks, usually on Thursdays at 10am PST (1pm EST). Seats are limited. To sign up for a particular webinar, see "How to Attend" below. Presentations may last anywhere from 20 to 90 minutes (with an estimate provided upfront).

If you missed a User Webinar, don't despair. We usually records them, including audio and screensharing. So, you can replay them any time. See below for the full list.

;Recent talks include:
* [[/Past_Topics#Expecting_the_Unexpected:_Coping_with_surprises_in_Probabilistic_and_Scenario_Forecasting|Expecting the Unexpected]] (07 April 2011)
* [[/Past_Topics#Optimizing_Parameters_in_a_Complex_Model_to_Match_Historical_Data|Optimizing Parameters in a Complex Model to Match Historical Data]] (31 Mar 2011)
* [[/Past_Topics#Interactive Optimization Workshop|Interactive Optimization Workshop]] (24 Mar 2011)
* [[/Past_Topics#Introduction_to_Structured_Optimization|Introduction to Structured Optimization]] (24 Feb 2011)

== Schedule of Upcoming Webinars ==

=== Optimization with Uncertainty ===

'''Date and Time:''' Thursday, 14 April 2011, 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Ph.D., Lumina Decision Systems
'''Register at''' [https://www2.gotomeeting.com/ojoin/657053954/182596]
'''Abstract'''

Analytica analyzes uncertainty by conducting a Monte Carlo analysis. When you optimize decision variables in a model containing uncertainty, you have a choice: You can perform one optimization over the Monte Carlo analysis, or you can perform a Monte Carlo sampling of optimizations (i.e., the Monte Carlo is inside the optimization, or the optimization is inside the Monte Carlo). The first case is used when the decision must be taken while the quantities are still uncertain. The second case is used when the values of the uncertain quantities will be resolved before the decisions are taken.

To illustrate, consider the situation faced by a relief organization that provides aid to victims of large natural disasters. In one situation, a decision must be made regarding how many resources to deploy to one particular location that has been hit by a large tsunami. At the time the decision must be made, the number of casualties is highly uncertain. In a different situation, the organization wants to characterize the uncertainty in its need for resources, given that it will optimally deploy resources in response to natural disasters as they occur.

=== Neural Networks ===

'''Date and Time:''' Thursday, 21 April 2011, 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Ph.D.

'''Abstract'''

A feed-forward artificial neural networks is a non-linear function that predicts one or more outputs from a set of inputs. These are usually used in two layers, where the first layer of inputs are weighted and summed, and then passed through a sigmoid function to determine the activations of a hidden layer, those those activations are weighted, summed and then passed through a sigmoid function to predict the final output. A training phase is used to adjust the weight to "fit" an example data set.

In this webinar, I'll create a nearal network model in Analytica and train it on example data as a demonstration of the use of structured optimization. It provides a simple and easily understood example of the use of intrinsic indexes in a structured optimization model, while at the same time introducing the basics of the interesting topic if neural networks.

=== Linearizing Optimization Models ===

=== [[Model building by mouse]] ===

'''Date and Time:''' (TBD)

'''Presenter''': (TBD)

'''Abstract'''

See Model building by mouse for the concept. The talk will also cover the TemplateInput and TemplateOutput attributes first introduced in 4.2 that make template modules possible, and thus expand the potential power and applicability of this modeling technique.

=== Saving Memory: Controlling when results are (and are not) cached ===

'''Date and Time:''' (TBD)

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

When a variable is computed, Analytica caches the result (i.e., keeps a copy around) so that when the same result is needed later, it does not need to be recomputed. Large array-valued results can end up consuming large amounts of memory, and lead to annoyances such as running out of available memory. There are instances where it isn't really necessary to cache a result -- either it will never be needed a second time, or it isn't a big deal to recompute it, or it is an exact copy of its parent (perhaps because only the result graph settings are different), so that by configuring that variable to not cache its result, or to release its cache as soon as all its children are computed, memory space can be recovered, perhaps allowing larger simulations to be conducted.

Analytica 4.2 introduces a new feature allowing you to configure when results are (or are not) cached. I'll demonstrate how to utilize this feature, and discuss limitations, such as situations where it would be a very bad idea not to cache.

If time allows (it probably will), I'll also throw in two other small memory-related controls new in 4.2. The [[WhatIf]]-style functions ([[WhatIf]], [[WhatIfAll]], [[Dydx]], [[Elasticity]], [[NlpDefine]]), i.e., functions that compute hypotheticals, have been modified in 4.2 to preserve previously computed values of other variables. This is a nice feature, but can impact tight-memory cases. I'll explain this change and how to avoid the extra memory consumption where appropriate. Second, I'll also show how Analytica's maximum working set size can be configured (on some operating systems), which can be used to keep other applications on your computer highly responsive even when large memory-intensive computations are churning away.

In Analytica 64-bit, you are less likely to encounter the problem of running out of memory, but you still may encounter performance slowdown from huge memory utilization. Some large models, requiring extensive memory resources, run smoothly even when requiring far more memory than there is RAM available, while other result in extensive ''thrashing'', taking very long to evaluate. I've found that differentiating factor has to do with the size of individual arrays within the model. Thrashing tends to occur when a single array consumes more than about 1/3 the available RAM, while if all arrays in the model remain small relative to available RAM, huge memory evaluations often run very smoothly. I'll relate these observations, and what that means with respect to sample size.

This talk is appropriate for advanced Analytica modelers.

=== The [[Dynamic]] Function and Dynamic Loops ===

'''Date and Time:''' (TBD)

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

The [[Dynamic]] function in Analytica 4.2 is a bit more flexible than previously. It is now possible for [[User-Defined Functions]] to be part of a dynamic loop, and there are cases where it is now possible to operate over the [[Time]] index where previous it was not. I'll discuss how dynamic models are evaluated, and how that differs from non-dynamic models, and cover some tricks for tracing evaluation and debugging complex dynamic models (which can often be challenging to debug). I'll also introduce how [[Dynamic]] can be used on an index other than the built-in [[Time]] index.

=== [[IntraTable]]s ===

'''Date and Time:''' (TBD)

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

An [[IntraTable]] is a variation of an edit [[Table]], where (like [[Table]]s), the cells may contain expressions, but unlike [[Table]]s, the expressions in each cell can refer to values of other cells. This may (quite appropriately) conjure up nightmares of spreadsheet hell. While this isn't something you'd want to use nonchalantly, there are cases when the ability to reference other cells within the same table comes with its benefits. Because the [[IntraTable]] allows any pattern of references (it doesn't have to be strictly left-to-right, for example), as long as a cell-cycle isn't created, it is possible to encode certain recurrences that would otherwise be quite difficult to encode.

== How to Attend ==

To attend, you need to sign up by contacting Lumina at mailto:webinars@lumina.com. Please sign up at least a day prior. Attendance is limited, so please don't sign up unless you sincerely intend to attend.

These Webinars are FREE to users who have an up-to-date Support for Analytica. If you are unsure, check with mailto:sales@lumina. For those without current support, the fee is US$50.

== How to be a Presenter ==

Being a presenter at an Analytica webinar provides an opportunity to make others in the Analytica community aware of your successes or capabilities. Consultants may find this an opportunity for exposure to others with particular modeling needs. Also, if you are an Analytica aficionado, this is a great opportunity to help others.

If you would like to be a presenter, submit your proposed topic to webinars@lumina.com and possible presentation times (include the time zone). We will schedule the GotoMeeting conference (you do not need a gotoMeeting subscription yourself) and we will make you presenter during the session, allowing you to share your screen while you talk. You will most likely make use of Power Point and a running Analytica during your presentation.

==Analytica User Group:([[Analytica_User_Group/Past_Topics#The_Large_Sample_Library|Large Sample Library]])==

== [[Analytica User Group/Past Topics|Archive of Past Topics]] ==

Visit the [[Analytica User Group/Past Topics|Archive of Past Topics]] for abstracts, example models, and recordings of previous Analytica User Group webinars. Previous topics that have been presented include:

* Guidelines for Model Transparency
* Automated Monitoring and Failure Detection
* Principal Component Analysis (PCA)
* Internal Rate of Return ([[IRR]]) and Modified Internal Rate of Return ([[MIRR]])
* Bond Portfolio Analysis
* Net Present Value ([[NPV]])
* The Analytica Wiki, and How to Contribute
* Data Center Capacity Planning
* Modeling the Precision Strike Process
* The Performance Profiler
* Variable Stiffness Cubic Splines
* Introduction to [[DetermTable]]s
* Importance Sampling (Bayesian posteriors)
* Importance Sampling (rare events)
* Statistical Functions
* [[Table Splicing]]
* Analytica Web Player
* [[SubTable|SubTables]]
* Creating Custom Distribution Functions
* Using the Check attribute to validate inputs and results
* The [[Dynamic]] Function
* [[Logistic Regression]]
* Creating Control Panels
* Statistical Functions
* Creating Scatter Plots
* Using [[Regression]]
* New Functions for Reading Directly from an Excel File
* Advanced Tornado Charts -- when inputs are Array-Valued
* Using the Analytica Decision Engine (ADE) from ASP.NET
* OLE Linking
* Tornado Charts
* Correlated and Multivariate Distributions
* Assessment of Probability Distributions
* Graph Style Templates
* Sneak preview of Analytica Web Publisher
* Querying an OLAP server
* Querying an ODBC relational database
* [[Self-Indexed Arrays|Self-Indexes]], Lists and [[Implicit Dimensions]]
* Flattening and Unflattening of Arrays
* Introduction to Arrays and Array Abstraction
* [[Local Indexes]]
* The [[Iterate]] Function
* The [[Using_References|Reference and Dereference Operators]]
* Modeling Utility Tariffs in Analytica
* Modeling Energy Efficiency in Large Data Centers
* Calling External Applications
* Introduction to Linear and Quadratic Programming
* Non-Linear Optimization
* Writing [[User-Defined Functions]]
* Modeling Markov Processes in Analytica
* Manipulating Dates in Analytica
* Button Scripting
* Manipulating Indexes and Arrays in Analytica Expressions
* Edit Table Enhancements in Analytica 4.0
* [[Handle]]s and [[Meta-Inference]]

== Potential future topics ==

If you would like to see a webinar on a given topic, please feel free to add it here. If you see a topic listed and would like to be a presenter, let us know.

* Statistical hypothesis testing.
** Standard textbook tests
** Computing p-values using Monte Carlo for complex and non-standard statistical models.

* Time-series analysis

* Using the interpolation functions, [[LinearInterp]], [[CubicInterp]], [[StepInterp]].

* Numeric precision - numeric round-off, underflow, etc., why they happen, what to do about it.

* Net Present Value and Internal Rate of Return -- introduction to the use of these commonly used metrics for quantifying decision quality.

* Using the ''Performance Profiler'' to understand where your model consumes time and memory resources.

* Producing graphs from ADE: Including how to serve graphs from web pages.

* Intracacies of the Domain attribute

* Mastering Array Abstraction

* Writing User-Defined Distribution functions.

* Getting data into Analytica.

* [[DetermTable]]s

* Large-scale sampling. Techniques when memory limitations constrain sampleSize.

* Creating User-Defined functions. Review of [[Function Parameter Qualifiers]].

* Understanding [[Evaluation Contexts]]

* Bayesian Inference

* Sensitivity Analysis.

* Dynamic Programming

* Approximate and Stochastic Dynamic Programming

== User Survey Results ==

During the first week of September, we sent out a survey to people who had attended webinars so far. Please continue providing us with feedback. Here is some feedback to date:

Level of difficult and speed:

So far, of those who answered this question, 25% say "a bit too easy", 25% say "too hard/fast", and 50% say "just right".

Topics requested for future webinars:

* How to run multiple iterations, e.g. 100 iterations with uncertainty sample of 1000.

* Sampling for rare events.

* Using the lognormal function.

* Financial modeling

* New array functionality (subtables, choices in tables)

* Optimizer !!!

* Dynamic models (twice requested)

* Choice of distributions

* Re-sampling and radomize methods and uncertainty sample size

* Tricks for sensitivity analysis

* Input and output nodes

* Importance analysis

= The Analytica Wiki =

The [[Analytica Wiki ]] contains many resources, including in-depth reference materials, relevant articles, example models, tutorials, etc., to help users master Analytica and find what they need. Even better, Analytica users can contribute! You can upload your own models, articles, expand on or correct materials that are there, etc., for the benefit of the entire Analytica community.

Analytica User Group

2011-03-16T19:44:02Z

Nkretz: /* Interactive Optimization Workshop */

[[/Past Topics|Past Topics]] >>

The Analytica User Group is a way for Analytica users to support each other by sharing tips and function libraries. It includes a webinar series with tutorials and demonstrations on key Analytica features.

= Webinar Series =

Analytica User Webinars are a great way to learn about key Analytica features and modeling skills. They are live demos, using Citrix Gotowebinar, and VOIP or your telephone, at your choice. Questions, comments, and tangents are welcome.

There are webinars most weeks, usually on Thursdays at 10am PST (1pm EST). Seats are limited. To sign up for a particular webinar, see "How to Attend" below. Presentations may last anywhere from 20 to 90 minutes (with an estimate provided upfront).

If you missed a User Webinar, don't despair. We usually records them, including audio and screensharing. So, you can replay them any time. See below for the full list.

;Recent talks include:
* [[/Past_Topics#Introduction_to_Structured_Optimization|Introduction to Structured Optimization]] (24 Feb 2011)
* [[/Past_Topics#Gentle_Introduction_to_Modeling_Uncertainty:_Webinar_Series|Gentle Introduction to Modeling Uncertainty]]
** [[/Past_Topics#Session_1:_Uncertainty_and_Probability|Session 1]]: (29 Apr 2010)[[/Past_Topics#Session_1:_Uncertainty_and_Probability|Introduction to Uncertainty, Probability]].
** [[/Past_Topics#Session_2:_Probability_Distributions|Session 2]]: (6 May 2010)[[/Past_Topics#Session_2:_Probability_Distributions|Probabiliity Distributions]]
** [[/Past_Topics#Session_3:_Monte_Carlo|Session 3]]: (13 May 2010)[[/Past_Topics#Session_3:_Monte_Carlo|Monte Carlo Simulation]]
** [[/Past_Topics#Session_4:_Measures_of_Risk_and_Utility|Session 4]]: (20 May 2010) [/Past_Topics#Session_4:_Measures_of_Risk_and_Utility|Measures of Risk and Utility]]
** [[/Past_Topics#Session_5:_Risk_Analysis_for_Portfolios|Session 5]]: (3 June 2010) [[/Past_Topics#Session_5:_Risk_Analysis_for_Portfolios|Risk Analysis for Portfolios]]
** [[/Past_Topics#Session_6:_Common_Parametric_Distributions|Session 6]]: (10 June 2010) [[/Past_Topics#Session_6:_Common_Parametric_Distributions|Common Parametric Distributions]]
** [[/Past_Topics#Session_7:_Expert_Assessment_of_Uncertainty|Session 7]]: (24 June 2010) [[/Past_Topics#Session_7:_Expert_Assessment_of_Uncertainty|Expert Assessment of Uncertainty]]
** [[/Past_Topics#Session_8:_Hypothesis_Testing|Session 8]]: (15 July 2010) [[/Past_Topics#Session_8:_Hypothesis Testing|Statistical Hypothesis Testing]]
* [[/Past_Topics#Regional_Weather_Data_Analysis|Regional Weather Data Analysis]] (22 Apr 2010)
* [[/Past_Topics#Step_Interpolation|Step Interpolation]] (8 Apr 2010)
* [[/Past Topics#Spearman_Rank_Correlation|Introduction to Spearman's Rank Correlation]] (25 Mar 2010)

== Schedule of Upcoming Webinars ==

=== Interactive Optimization Workshop ===

'''Date and Time:''' Thursday, 24 March 2011, 10:00am Pacific Daylight Time

'''Presenter:''' Paul Sanford, Lumina Decision Systems

'''Abstract'''

This is an interactive workshop where you will learn the basics of creating Structured Optimization models and challenge yourself to set up and solve some basic examples on your own! No prior training in optimization is required. '''Trial Downloads'''[http://www.lumina.com/products/optimizer-trial/] of Analytica Optimizer 4.3 are now available. Attendees are encouraged to have Analytica Optimizer 4.3 installed and running during the workshop.

'''Space is limited.'''
Reserve your Webinar seat now at:
https://www2.gotomeeting.com/register/287410707

=== Optimizing Parameters in a Complex Model to Match Historical Data ===

'''Date and Time:''' Thursday, 31 March 2011, 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Ph.D., Lumina Decision Systems

'''Abstract'''

Almost all quantitative models have parameters that must be assessed by experts or estimated from historical data. Estimation from historical data can be complicated by the presence of variables that are either unobservable or unavailable in the historical record. Maximum likelihood estimation addresses this by finding the parameter settings that maximize the likelihood of the historical data predicted by the model. In this talk, I will formulate the parameter fitting task as a structured optimization problem (NLP), providing a hands-on demonstration of the new structured optimization features in Analytica 4.3.

'''Space is limited.'''
Reserve your Webinar seat now at:
https://www2.gotomeeting.com/register/913543867

=== Optimization with Uncertainty ===

'''Date and Time:''' Thursday, 6 April 2011, 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Ph.D., Lumina Decision Systems

'''Abstract'''

Analytica analyzes uncertainty by conducting a Monte Carlo analysis. When you optimize decision variables in a model containing uncertainty, you have a choice: You can perform one optimization over the Monte Carlo analysis, or you can perform a Monte Carlo sampling of optimizations (i.e., the Monte Carlo is inside the optimization, or the optimization is inside the Monte Carlo). The first case is used when the decision must be taken while the quantities are still uncertain. The second case is used when the values of the uncertain quantities will be resolved before the decisions are taken.

To illustrate, consider the situation faced by a relief organization that provides aid to victims of large natural disasters. In one situation, a decision must be made regarding how many resources to deploy to one particular location that has been hit by a large tsunami. At the time the decision must be made, the number of casualties is highly uncertain. In a different situation, the organization wants to characterize the uncertainty in its need for resources, given that it will optimally deploy resources in response to natural disasters as they occur.

=== Neural Networks ===

'''Date and Time:''' Thursday, 13 April 2011, 10:00am Pacific Daylight Time

=== Linearizing Optimization Models ===

=== [[Model building by mouse]] ===

'''Date and Time:''' (TBD)

'''Presenter''': (TBD)

'''Abstract'''

See Model building by mouse for the concept. The talk will also cover the TemplateInput and TemplateOutput attributes first introduced in 4.2 that make template modules possible, and thus expand the potential power and applicability of this modeling technique.

=== Saving Memory: Controlling when results are (and are not) cached ===

'''Date and Time:''' (TBD)

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

When a variable is computed, Analytica caches the result (i.e., keeps a copy around) so that when the same result is needed later, it does not need to be recomputed. Large array-valued results can end up consuming large amounts of memory, and lead to annoyances such as running out of available memory. There are instances where it isn't really necessary to cache a result -- either it will never be needed a second time, or it isn't a big deal to recompute it, or it is an exact copy of its parent (perhaps because only the result graph settings are different), so that by configuring that variable to not cache its result, or to release its cache as soon as all its children are computed, memory space can be recovered, perhaps allowing larger simulations to be conducted.

Analytica 4.2 introduces a new feature allowing you to configure when results are (or are not) cached. I'll demonstrate how to utilize this feature, and discuss limitations, such as situations where it would be a very bad idea not to cache.

If time allows (it probably will), I'll also throw in two other small memory-related controls new in 4.2. The [[WhatIf]]-style functions ([[WhatIf]], [[WhatIfAll]], [[Dydx]], [[Elasticity]], [[NlpDefine]]), i.e., functions that compute hypotheticals, have been modified in 4.2 to preserve previously computed values of other variables. This is a nice feature, but can impact tight-memory cases. I'll explain this change and how to avoid the extra memory consumption where appropriate. Second, I'll also show how Analytica's maximum working set size can be configured (on some operating systems), which can be used to keep other applications on your computer highly responsive even when large memory-intensive computations are churning away.

In Analytica 64-bit, you are less likely to encounter the problem of running out of memory, but you still may encounter performance slowdown from huge memory utilization. Some large models, requiring extensive memory resources, run smoothly even when requiring far more memory than there is RAM available, while other result in extensive ''thrashing'', taking very long to evaluate. I've found that differentiating factor has to do with the size of individual arrays within the model. Thrashing tends to occur when a single array consumes more than about 1/3 the available RAM, while if all arrays in the model remain small relative to available RAM, huge memory evaluations often run very smoothly. I'll relate these observations, and what that means with respect to sample size.

This talk is appropriate for advanced Analytica modelers.

=== The [[Dynamic]] Function and Dynamic Loops ===

'''Date and Time:''' (TBD)

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

The [[Dynamic]] function in Analytica 4.2 is a bit more flexible than previously. It is now possible for [[User-Defined Functions]] to be part of a dynamic loop, and there are cases where it is now possible to operate over the [[Time]] index where previous it was not. I'll discuss how dynamic models are evaluated, and how that differs from non-dynamic models, and cover some tricks for tracing evaluation and debugging complex dynamic models (which can often be challenging to debug). I'll also introduce how [[Dynamic]] can be used on an index other than the built-in [[Time]] index.

=== [[IntraTable]]s ===

'''Date and Time:''' (TBD)

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

An [[IntraTable]] is a variation of an edit [[Table]], where (like [[Table]]s), the cells may contain expressions, but unlike [[Table]]s, the expressions in each cell can refer to values of other cells. This may (quite appropriately) conjure up nightmares of spreadsheet hell. While this isn't something you'd want to use nonchalantly, there are cases when the ability to reference other cells within the same table comes with its benefits. Because the [[IntraTable]] allows any pattern of references (it doesn't have to be strictly left-to-right, for example), as long as a cell-cycle isn't created, it is possible to encode certain recurrences that would otherwise be quite difficult to encode.

== How to Attend ==

To attend, you need to sign up by contacting Lumina at mailto:webinars@lumina.com. Please sign up at least a day prior. Attendance is limited, so please don't sign up unless you sincerely intend to attend.

These Webinars are FREE to users who have an up-to-date Support for Analytica. If you are unsure, check with mailto:sales@lumina. For those without current support, the fee is US$50.

== How to be a Presenter ==

Being a presenter at an Analytica webinar provides an opportunity to make others in the Analytica community aware of your successes or capabilities. Consultants may find this an opportunity for exposure to others with particular modeling needs. Also, if you are an Analytica aficionado, this is a great opportunity to help others.

If you would like to be a presenter, submit your proposed topic to webinars@lumina.com and possible presentation times (include the time zone). We will schedule the GotoMeeting conference (you do not need a gotoMeeting subscription yourself) and we will make you presenter during the session, allowing you to share your screen while you talk. You will most likely make use of Power Point and a running Analytica during your presentation.

==Analytica User Group:([[Analytica_User_Group/Past_Topics#The_Large_Sample_Library|Large Sample Library]])==

== [[Analytica User Group/Past Topics|Archive of Past Topics]] ==

Visit the [[Analytica User Group/Past Topics|Archive of Past Topics]] for abstracts, example models, and recordings of previous Analytica User Group webinars. Previous topics that have been presented include:

* Guidelines for Model Transparency
* Automated Monitoring and Failure Detection
* Principal Component Analysis (PCA)
* Internal Rate of Return ([[IRR]]) and Modified Internal Rate of Return ([[MIRR]])
* Bond Portfolio Analysis
* Net Present Value ([[NPV]])
* The Analytica Wiki, and How to Contribute
* Data Center Capacity Planning
* Modeling the Precision Strike Process
* The Performance Profiler
* Variable Stiffness Cubic Splines
* Introduction to [[DetermTable]]s
* Importance Sampling (Bayesian posteriors)
* Importance Sampling (rare events)
* Statistical Functions
* [[Table Splicing]]
* Analytica Web Player
* [[SubTable|SubTables]]
* Creating Custom Distribution Functions
* Using the Check attribute to validate inputs and results
* The [[Dynamic]] Function
* [[Logistic Regression]]
* Creating Control Panels
* Statistical Functions
* Creating Scatter Plots
* Using [[Regression]]
* New Functions for Reading Directly from an Excel File
* Advanced Tornado Charts -- when inputs are Array-Valued
* Using the Analytica Decision Engine (ADE) from ASP.NET
* OLE Linking
* Tornado Charts
* Correlated and Multivariate Distributions
* Assessment of Probability Distributions
* Graph Style Templates
* Sneak preview of Analytica Web Publisher
* Querying an OLAP server
* Querying an ODBC relational database
* [[Self-Indexed Arrays|Self-Indexes]], Lists and [[Implicit Dimensions]]
* Flattening and Unflattening of Arrays
* Introduction to Arrays and Array Abstraction
* [[Local Indexes]]
* The [[Iterate]] Function
* The [[Using_References|Reference and Dereference Operators]]
* Modeling Utility Tariffs in Analytica
* Modeling Energy Efficiency in Large Data Centers
* Calling External Applications
* Introduction to Linear and Quadratic Programming
* Non-Linear Optimization
* Writing [[User-Defined Functions]]
* Modeling Markov Processes in Analytica
* Manipulating Dates in Analytica
* Button Scripting
* Manipulating Indexes and Arrays in Analytica Expressions
* Edit Table Enhancements in Analytica 4.0
* [[Handle]]s and [[Meta-Inference]]

== Potential future topics ==

If you would like to see a webinar on a given topic, please feel free to add it here. If you see a topic listed and would like to be a presenter, let us know.

* Statistical hypothesis testing.
** Standard textbook tests
** Computing p-values using Monte Carlo for complex and non-standard statistical models.

* Time-series analysis

* Using the interpolation functions, [[LinearInterp]], [[CubicInterp]], [[StepInterp]].

* Numeric precision - numeric round-off, underflow, etc., why they happen, what to do about it.

* Net Present Value and Internal Rate of Return -- introduction to the use of these commonly used metrics for quantifying decision quality.

* Using the ''Performance Profiler'' to understand where your model consumes time and memory resources.

* Producing graphs from ADE: Including how to serve graphs from web pages.

* Intracacies of the Domain attribute

* Mastering Array Abstraction

* Writing User-Defined Distribution functions.

* Getting data into Analytica.

* [[DetermTable]]s

* Large-scale sampling. Techniques when memory limitations constrain sampleSize.

* Creating User-Defined functions. Review of [[Function Parameter Qualifiers]].

* Understanding [[Evaluation Contexts]]

* Bayesian Inference

* Sensitivity Analysis.

* Dynamic Programming

* Approximate and Stochastic Dynamic Programming

== User Survey Results ==

During the first week of September, we sent out a survey to people who had attended webinars so far. Please continue providing us with feedback. Here is some feedback to date:

Level of difficult and speed:

So far, of those who answered this question, 25% say "a bit too easy", 25% say "too hard/fast", and 50% say "just right".

Topics requested for future webinars:

* How to run multiple iterations, e.g. 100 iterations with uncertainty sample of 1000.

* Sampling for rare events.

* Using the lognormal function.

* Financial modeling

* New array functionality (subtables, choices in tables)

* Optimizer !!!

* Dynamic models (twice requested)

* Choice of distributions

* Re-sampling and radomize methods and uncertainty sample size

* Tricks for sensitivity analysis

* Input and output nodes

* Importance analysis

= The Analytica Wiki =

The [[Analytica Wiki ]] contains many resources, including in-depth reference materials, relevant articles, example models, tutorials, etc., to help users master Analytica and find what they need. Even better, Analytica users can contribute! You can upload your own models, articles, expand on or correct materials that are there, etc., for the benefit of the entire Analytica community.

Analytica User Group

2011-03-16T19:42:01Z

Nkretz: /* Optimizing Parameters in a Complex Model to Match Historical Data */

[[/Past Topics|Past Topics]] >>

The Analytica User Group is a way for Analytica users to support each other by sharing tips and function libraries. It includes a webinar series with tutorials and demonstrations on key Analytica features.

= Webinar Series =

Analytica User Webinars are a great way to learn about key Analytica features and modeling skills. They are live demos, using Citrix Gotowebinar, and VOIP or your telephone, at your choice. Questions, comments, and tangents are welcome.

There are webinars most weeks, usually on Thursdays at 10am PST (1pm EST). Seats are limited. To sign up for a particular webinar, see "How to Attend" below. Presentations may last anywhere from 20 to 90 minutes (with an estimate provided upfront).

If you missed a User Webinar, don't despair. We usually records them, including audio and screensharing. So, you can replay them any time. See below for the full list.

;Recent talks include:
* [[/Past_Topics#Introduction_to_Structured_Optimization|Introduction to Structured Optimization]] (24 Feb 2011)
* [[/Past_Topics#Gentle_Introduction_to_Modeling_Uncertainty:_Webinar_Series|Gentle Introduction to Modeling Uncertainty]]
** [[/Past_Topics#Session_1:_Uncertainty_and_Probability|Session 1]]: (29 Apr 2010)[[/Past_Topics#Session_1:_Uncertainty_and_Probability|Introduction to Uncertainty, Probability]].
** [[/Past_Topics#Session_2:_Probability_Distributions|Session 2]]: (6 May 2010)[[/Past_Topics#Session_2:_Probability_Distributions|Probabiliity Distributions]]
** [[/Past_Topics#Session_3:_Monte_Carlo|Session 3]]: (13 May 2010)[[/Past_Topics#Session_3:_Monte_Carlo|Monte Carlo Simulation]]
** [[/Past_Topics#Session_4:_Measures_of_Risk_and_Utility|Session 4]]: (20 May 2010) [/Past_Topics#Session_4:_Measures_of_Risk_and_Utility|Measures of Risk and Utility]]
** [[/Past_Topics#Session_5:_Risk_Analysis_for_Portfolios|Session 5]]: (3 June 2010) [[/Past_Topics#Session_5:_Risk_Analysis_for_Portfolios|Risk Analysis for Portfolios]]
** [[/Past_Topics#Session_6:_Common_Parametric_Distributions|Session 6]]: (10 June 2010) [[/Past_Topics#Session_6:_Common_Parametric_Distributions|Common Parametric Distributions]]
** [[/Past_Topics#Session_7:_Expert_Assessment_of_Uncertainty|Session 7]]: (24 June 2010) [[/Past_Topics#Session_7:_Expert_Assessment_of_Uncertainty|Expert Assessment of Uncertainty]]
** [[/Past_Topics#Session_8:_Hypothesis_Testing|Session 8]]: (15 July 2010) [[/Past_Topics#Session_8:_Hypothesis Testing|Statistical Hypothesis Testing]]
* [[/Past_Topics#Regional_Weather_Data_Analysis|Regional Weather Data Analysis]] (22 Apr 2010)
* [[/Past_Topics#Step_Interpolation|Step Interpolation]] (8 Apr 2010)
* [[/Past Topics#Spearman_Rank_Correlation|Introduction to Spearman's Rank Correlation]] (25 Mar 2010)

== Schedule of Upcoming Webinars ==

=== Interactive Optimization Workshop ===

'''Date and Time:''' Thursday, 24 March 2011, 10:00am Pacific Daylight Time

'''Presenter:''' Paul Sanford, Lumina Decision Systems

'''Abstract'''

This is an interactive workshop where you will learn the basics of creating Structured Optimization models and challenge yourself to set up and solve some basic examples on your own! No prior training in optimization is required. '''Trial Downloads'''[http://www.lumina.com/products/optimizer-trial/] of Analytica Optimizer 4.3 are now available. Attendees are encouraged to have Analytica Optimizer 4.3 installed and running during the workshop.

=== Optimizing Parameters in a Complex Model to Match Historical Data ===

'''Date and Time:''' Thursday, 31 March 2011, 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Ph.D., Lumina Decision Systems

'''Abstract'''

Almost all quantitative models have parameters that must be assessed by experts or estimated from historical data. Estimation from historical data can be complicated by the presence of variables that are either unobservable or unavailable in the historical record. Maximum likelihood estimation addresses this by finding the parameter settings that maximize the likelihood of the historical data predicted by the model. In this talk, I will formulate the parameter fitting task as a structured optimization problem (NLP), providing a hands-on demonstration of the new structured optimization features in Analytica 4.3.

'''Space is limited.'''
Reserve your Webinar seat now at:
https://www2.gotomeeting.com/register/913543867

=== Optimization with Uncertainty ===

'''Date and Time:''' Thursday, 6 April 2011, 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Ph.D., Lumina Decision Systems

'''Abstract'''

Analytica analyzes uncertainty by conducting a Monte Carlo analysis. When you optimize decision variables in a model containing uncertainty, you have a choice: You can perform one optimization over the Monte Carlo analysis, or you can perform a Monte Carlo sampling of optimizations (i.e., the Monte Carlo is inside the optimization, or the optimization is inside the Monte Carlo). The first case is used when the decision must be taken while the quantities are still uncertain. The second case is used when the values of the uncertain quantities will be resolved before the decisions are taken.

To illustrate, consider the situation faced by a relief organization that provides aid to victims of large natural disasters. In one situation, a decision must be made regarding how many resources to deploy to one particular location that has been hit by a large tsunami. At the time the decision must be made, the number of casualties is highly uncertain. In a different situation, the organization wants to characterize the uncertainty in its need for resources, given that it will optimally deploy resources in response to natural disasters as they occur.

=== Neural Networks ===

'''Date and Time:''' Thursday, 13 April 2011, 10:00am Pacific Daylight Time

=== Linearizing Optimization Models ===

=== [[Model building by mouse]] ===

'''Date and Time:''' (TBD)

'''Presenter''': (TBD)

'''Abstract'''

See Model building by mouse for the concept. The talk will also cover the TemplateInput and TemplateOutput attributes first introduced in 4.2 that make template modules possible, and thus expand the potential power and applicability of this modeling technique.

=== Saving Memory: Controlling when results are (and are not) cached ===

'''Date and Time:''' (TBD)

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

When a variable is computed, Analytica caches the result (i.e., keeps a copy around) so that when the same result is needed later, it does not need to be recomputed. Large array-valued results can end up consuming large amounts of memory, and lead to annoyances such as running out of available memory. There are instances where it isn't really necessary to cache a result -- either it will never be needed a second time, or it isn't a big deal to recompute it, or it is an exact copy of its parent (perhaps because only the result graph settings are different), so that by configuring that variable to not cache its result, or to release its cache as soon as all its children are computed, memory space can be recovered, perhaps allowing larger simulations to be conducted.

Analytica 4.2 introduces a new feature allowing you to configure when results are (or are not) cached. I'll demonstrate how to utilize this feature, and discuss limitations, such as situations where it would be a very bad idea not to cache.

If time allows (it probably will), I'll also throw in two other small memory-related controls new in 4.2. The [[WhatIf]]-style functions ([[WhatIf]], [[WhatIfAll]], [[Dydx]], [[Elasticity]], [[NlpDefine]]), i.e., functions that compute hypotheticals, have been modified in 4.2 to preserve previously computed values of other variables. This is a nice feature, but can impact tight-memory cases. I'll explain this change and how to avoid the extra memory consumption where appropriate. Second, I'll also show how Analytica's maximum working set size can be configured (on some operating systems), which can be used to keep other applications on your computer highly responsive even when large memory-intensive computations are churning away.

In Analytica 64-bit, you are less likely to encounter the problem of running out of memory, but you still may encounter performance slowdown from huge memory utilization. Some large models, requiring extensive memory resources, run smoothly even when requiring far more memory than there is RAM available, while other result in extensive ''thrashing'', taking very long to evaluate. I've found that differentiating factor has to do with the size of individual arrays within the model. Thrashing tends to occur when a single array consumes more than about 1/3 the available RAM, while if all arrays in the model remain small relative to available RAM, huge memory evaluations often run very smoothly. I'll relate these observations, and what that means with respect to sample size.

This talk is appropriate for advanced Analytica modelers.

=== The [[Dynamic]] Function and Dynamic Loops ===

'''Date and Time:''' (TBD)

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

The [[Dynamic]] function in Analytica 4.2 is a bit more flexible than previously. It is now possible for [[User-Defined Functions]] to be part of a dynamic loop, and there are cases where it is now possible to operate over the [[Time]] index where previous it was not. I'll discuss how dynamic models are evaluated, and how that differs from non-dynamic models, and cover some tricks for tracing evaluation and debugging complex dynamic models (which can often be challenging to debug). I'll also introduce how [[Dynamic]] can be used on an index other than the built-in [[Time]] index.

=== [[IntraTable]]s ===

'''Date and Time:''' (TBD)

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

An [[IntraTable]] is a variation of an edit [[Table]], where (like [[Table]]s), the cells may contain expressions, but unlike [[Table]]s, the expressions in each cell can refer to values of other cells. This may (quite appropriately) conjure up nightmares of spreadsheet hell. While this isn't something you'd want to use nonchalantly, there are cases when the ability to reference other cells within the same table comes with its benefits. Because the [[IntraTable]] allows any pattern of references (it doesn't have to be strictly left-to-right, for example), as long as a cell-cycle isn't created, it is possible to encode certain recurrences that would otherwise be quite difficult to encode.

== How to Attend ==

To attend, you need to sign up by contacting Lumina at mailto:webinars@lumina.com. Please sign up at least a day prior. Attendance is limited, so please don't sign up unless you sincerely intend to attend.

These Webinars are FREE to users who have an up-to-date Support for Analytica. If you are unsure, check with mailto:sales@lumina. For those without current support, the fee is US$50.

== How to be a Presenter ==

Being a presenter at an Analytica webinar provides an opportunity to make others in the Analytica community aware of your successes or capabilities. Consultants may find this an opportunity for exposure to others with particular modeling needs. Also, if you are an Analytica aficionado, this is a great opportunity to help others.

If you would like to be a presenter, submit your proposed topic to webinars@lumina.com and possible presentation times (include the time zone). We will schedule the GotoMeeting conference (you do not need a gotoMeeting subscription yourself) and we will make you presenter during the session, allowing you to share your screen while you talk. You will most likely make use of Power Point and a running Analytica during your presentation.

==Analytica User Group:([[Analytica_User_Group/Past_Topics#The_Large_Sample_Library|Large Sample Library]])==

== [[Analytica User Group/Past Topics|Archive of Past Topics]] ==

Visit the [[Analytica User Group/Past Topics|Archive of Past Topics]] for abstracts, example models, and recordings of previous Analytica User Group webinars. Previous topics that have been presented include:

* Guidelines for Model Transparency
* Automated Monitoring and Failure Detection
* Principal Component Analysis (PCA)
* Internal Rate of Return ([[IRR]]) and Modified Internal Rate of Return ([[MIRR]])
* Bond Portfolio Analysis
* Net Present Value ([[NPV]])
* The Analytica Wiki, and How to Contribute
* Data Center Capacity Planning
* Modeling the Precision Strike Process
* The Performance Profiler
* Variable Stiffness Cubic Splines
* Introduction to [[DetermTable]]s
* Importance Sampling (Bayesian posteriors)
* Importance Sampling (rare events)
* Statistical Functions
* [[Table Splicing]]
* Analytica Web Player
* [[SubTable|SubTables]]
* Creating Custom Distribution Functions
* Using the Check attribute to validate inputs and results
* The [[Dynamic]] Function
* [[Logistic Regression]]
* Creating Control Panels
* Statistical Functions
* Creating Scatter Plots
* Using [[Regression]]
* New Functions for Reading Directly from an Excel File
* Advanced Tornado Charts -- when inputs are Array-Valued
* Using the Analytica Decision Engine (ADE) from ASP.NET
* OLE Linking
* Tornado Charts
* Correlated and Multivariate Distributions
* Assessment of Probability Distributions
* Graph Style Templates
* Sneak preview of Analytica Web Publisher
* Querying an OLAP server
* Querying an ODBC relational database
* [[Self-Indexed Arrays|Self-Indexes]], Lists and [[Implicit Dimensions]]
* Flattening and Unflattening of Arrays
* Introduction to Arrays and Array Abstraction
* [[Local Indexes]]
* The [[Iterate]] Function
* The [[Using_References|Reference and Dereference Operators]]
* Modeling Utility Tariffs in Analytica
* Modeling Energy Efficiency in Large Data Centers
* Calling External Applications
* Introduction to Linear and Quadratic Programming
* Non-Linear Optimization
* Writing [[User-Defined Functions]]
* Modeling Markov Processes in Analytica
* Manipulating Dates in Analytica
* Button Scripting
* Manipulating Indexes and Arrays in Analytica Expressions
* Edit Table Enhancements in Analytica 4.0
* [[Handle]]s and [[Meta-Inference]]

== Potential future topics ==

If you would like to see a webinar on a given topic, please feel free to add it here. If you see a topic listed and would like to be a presenter, let us know.

* Statistical hypothesis testing.
** Standard textbook tests
** Computing p-values using Monte Carlo for complex and non-standard statistical models.

* Time-series analysis

* Using the interpolation functions, [[LinearInterp]], [[CubicInterp]], [[StepInterp]].

* Numeric precision - numeric round-off, underflow, etc., why they happen, what to do about it.

* Net Present Value and Internal Rate of Return -- introduction to the use of these commonly used metrics for quantifying decision quality.

* Using the ''Performance Profiler'' to understand where your model consumes time and memory resources.

* Producing graphs from ADE: Including how to serve graphs from web pages.

* Intracacies of the Domain attribute

* Mastering Array Abstraction

* Writing User-Defined Distribution functions.

* Getting data into Analytica.

* [[DetermTable]]s

* Large-scale sampling. Techniques when memory limitations constrain sampleSize.

* Creating User-Defined functions. Review of [[Function Parameter Qualifiers]].

* Understanding [[Evaluation Contexts]]

* Bayesian Inference

* Sensitivity Analysis.

* Dynamic Programming

* Approximate and Stochastic Dynamic Programming

== User Survey Results ==

During the first week of September, we sent out a survey to people who had attended webinars so far. Please continue providing us with feedback. Here is some feedback to date:

Level of difficult and speed:

So far, of those who answered this question, 25% say "a bit too easy", 25% say "too hard/fast", and 50% say "just right".

Topics requested for future webinars:

* How to run multiple iterations, e.g. 100 iterations with uncertainty sample of 1000.

* Sampling for rare events.

* Using the lognormal function.

* Financial modeling

* New array functionality (subtables, choices in tables)

* Optimizer !!!

* Dynamic models (twice requested)

* Choice of distributions

* Re-sampling and radomize methods and uncertainty sample size

* Tricks for sensitivity analysis

* Input and output nodes

* Importance analysis

= The Analytica Wiki =

The [[Analytica Wiki ]] contains many resources, including in-depth reference materials, relevant articles, example models, tutorials, etc., to help users master Analytica and find what they need. Even better, Analytica users can contribute! You can upload your own models, articles, expand on or correct materials that are there, etc., for the benefit of the entire Analytica community.

Analytica Cloud Platform

2010-08-13T18:42:24Z

Nkretz: /* B. Model Reviewer plan */

=Analytica Web Player=

The Analytica Web Player (AWP) lets anyone view and run an Analytica model via a web browser. It also makes it easy to create web applications with Analytica. Users can open a model, view diagrams and objects, change input variables, and view results as tables and graphs via common web browser, such as Internet Explorer, Firefox, or Safari. AWP runs the Analytica models on a server computer using ADE (the Analytica Decision Engine).

AWP offers several advantages over Analytica Player for distributing models:
* Users can review and run Analytica models without having to download or install any new software on their desktop -- they don't need to consult their IT department. You can invite model reviewers or users simply by emailing them the URL of a model in AWP.
* Model authors can be sure that end users are using the latest version of models and data posted on the server, without having to worry about distributing updates to users individually.
* By using password-protected AWP accounts, you can reduce the possibility that sensitive or proprietary models will be seen by unauthorized people. (You can also use the information hiding features of Analytica Enterprise to hide selected data or formulas.)
* With the Application Subscription to AWP, users can save changes to their models for use in later sessions -- similar to the Analytica Power Player, but not possible with the free Analytica Player.
* Once your organization purchases a subscription, you can make Analytica models accessible to many users, without have to purchase an extra license for each user -- as you would with the Analytica Power Player.

Model authors can upload models into AWP directly from their desktop. Users can save changed models, to revisit them in later sessions. Usually, AWP directories are password protected, so only authorized users can view and use models. But, we also make a free AWP directory available for people who want to share their models openly.

We welcome your comments. Please tell us what you think, including how you would like to use AWP, and any additional features you would like.

== AWP is like the Analytica Player ==

AWP offers almost all the features of desktop Analytica Player. It lets you open a model, view diagrams, object windows, and results as graphs or tables. It lets you change any variable designated as an input, including as an Edit table; but it won't let you change other variables or create new objects.

Soon, like the Power Player, it will let you save a changed model so you can continue with the changed model in a later session.

== How AWP improves on Analytica Player ==

AWP offers several enhancements that are not (yet) available in desktop Analytica:

* When you move the cursor over a node (without clicking), it highlights the node with a light rectangle around it. After a couple of seconds, it shows the description of the node (if it has one) in a popup balloon.
* Like most web applications, you use ''single'' not double clicks to drill down -- for example, to open a module diagram.
* It can display a result table or graph, or edit table, ''embedded'' in a diagram, instead of having to open the result or edit table in a separate window. As model author in desktop Analytica, you simply make the height of the input or output node greater than four times the default size, i.e. 104 pixels (52 nodesize units). When you upload and open the model with AWP, the graph or table appears embedded in its parent diagram, in the rectangle with size and location specified for the node.

== AWP access ==

=== A. Demo and Trial Access ===

* If you haven't seen AWP in action, you can view a few example models in AWP.
** [http://www.analyticawebplayer.com/2.0/Client/AwpClient.aspx?inviteId=3&inviteCode=820186&subName=awp%20demos Play the Rent vs. Buy model in AWP.] The Rent vs. Buy model is an Analytica example model used in the Analytica Tutorial. It's a financial model comparing the Net Present Value of renting vs. buying a house. Model navigation is fairly intuitive. You can switch diagrams by clicking on modules or selecting different modules in the outline tree located on the left hide side of the diagram. Clicking on variable nodes generally displays the node's result. Hover the mouse over a node to see its description. One hint, you can right click on a node and select Object view from the popup menu to view the node's attributes in the object tab, notably the node's definition can be viewed here.
** [http://www.analyticawebplayer.com/2.0/Client/AwpClient.aspx?inviteId=2&inviteCode=784155&subName=awp%20demos Play the Foxes and Hares model in AWP.] The Foxes and Hares model is preditor / prey example mode also used in the Tutorial. When playing this model AWP adds beveled gradients and drop shadows to nodes on the influence diagram. Also, the default tabbed UI has been removed. Node descriptions and definitions can be seen by hovering over a node, and model results are displayed directly on the diagram.
* Sign up for a '''free Trial''' access to AWP at: [http://lumina.com/ana/trialAWP.htm The Analytica Web Player]. Then '''upload your own models''' and see how they work. Explore the features for inviting others to view your models, and for controlling who has access to your models.

=== B. Individual Subscription plan ===

Features for Admin (Subscriber)
* No limit to number of models posted.
* Single project directory.
* Can view how many user sessions are still available in month.
* Limit of 25 credits per month.
* Extra AWP credits can be bought for $1/credit in blocks. See below to buy blocks of additional credits.

Features for End Users:
* Can change inputs, evaluate results, browse model.
* Cannot save their inputs/changes

Price: $25/month or $200/year

=== C. Group Subscription Plan ===

Features for Admin (Subscriber)
* No limit to number of models posted.
* Password protect model use.
* Model Customization (ie. change logo, etc.)
* Multiple project directories.
* Can view how many user sessions are still available in month.
* Limit of 250 credits per month.
* Extra AWP credits can be bought for $1/credit in blocks. See below to buy blocks of additional credits.

Features for End Users:
* Database querying.
* Non-anonymous reviewers can save their inputs as a snapshot that can be loaded by that same user later, or reviewed by the subscriber

Price: $250/month or $2000/year

== Other differences ==

===UI Differences===
AWP has a few differences from Analytica in its user interface:

* It only shows one window at a time (plus optionally an outline window on the left)
* A single click on a variable shows its ''result'', as a table or graph, not object window.
* It offers tabs across the top to show the parent diagram, object view, table, or graph for the selected object.
* The expandable Outline view shows only modules, not variables or other objects. You can switch off display of the Outline by [Fred please complete]

===Linked Modules/Libraries===

* If your Analytica model contains linked libraries or linked modules, you can still play it on AWP, but it may require some tweaking.
* The simplest solution is to convert the linked libraries and linked modules into embedded modules and embedded libraries.
** To do this, e.g. for a linked module, bring up the object window for the linked module, then change the class from linked module to an embedded module. Then save the model file, and upload it onto AWP.
** Another way to do this especially if you've got multiple modules/libraries, is to select 'Save a copy in ...' from Analytica's file menu, and in the dialog that appears, check the 'Save everything in one file by embedding linked modules' checkbox. See screenshot below. Note this feature was adding in Anaytica 4.2 and is not available in earlier releases.

[[Image:Save everything in one file.png]]

* Another solution is to upload the linked modules and linked libraries onto the AWP server. In this case, you need to be sure that linked modules and linked libraries are in the same directory as your model fie. So when all the files are uploaded onto the server and the model file is opened, AWP will find and successfully load the linked modules and linked libraries.

== Features of Analytica not available in AWP ==

These features of Analytica Player are not (yet) available in AWP:
* Dialog boxes to change computation and display options, such as the Graph setup, Number format, Diagram and Node styles, Uncertainty Setup, and Preferences dialogs. It uses whatever options and styles you chose in Analytica before uploading the model to AWP.
* Slicer menus for multidimensional results that were introduced in Analytica 4.0. E.g. Color key slicer, Symbol key slicer, Symbol Size Key slicer and bar origin slicer.
* Clickable references.
* It has limited ability to copy and paste a subset of values from and to a table, and no ability to copy diagrams and graphs (except if you use Snagit or a similar application for selecting material to copy).
* No support for 'Check' attribute
* No support for 'MsgBox' function
* Other features which are new in Analytica 4.0/4.1 are being added to AWP. If you see something missing that you would like to have, let us know.

== Features of AWP not available in Analytica ==

AWP offers several options to modify the style of the user interface:

'''Tables and graphs in the diagram'''
* Embed edit tables and result tables and graphs in their parent diagram, so you can see the diagram and results in the Browser window.
* Reserve a space in a diagram to show the edit table, or result table or graph for any node you select in the diagram. This is especially handy for web applications.

'''[[AWP Rendering tables and graphs on the diagram|Click here for more info...]]

'''Using Awp_attrib attribute settings'''

* Don't display the the outline view (the default is on.) The outline is useful to help users navigate around large models, but not needed for small models or when you don't want users to look at all its details.
* Display only the top diagram, and not any submodules -- especially useful for web applications.
* Add bevel and shadows to nodes to make diagrams appear more dramatic.
* Tell AWP not to display things, e.g. don't allow users to save, or restrict model browsing so certain parts of your model are not accessible.

You can control these options with keywords added to a new attribute called '''Awp_attrib'''. See '''[[AWP Attribute Values]]''' for details.

== Enhancement requests==

* Do not show index selectors for tables and graphs. Currently, it only shows the selectors for 2D results, not the slicers for 3rd and higher dimensions anyway. For most applications, the model author selects the best view, and showing these options to end user adds needless complication.

== AWP Attribute ==

When creating a model in Desktop Analytica you can add an attribute Awp_attrib which can be used to specify certain behaviors when the model is played in AWP. [[AWP Attribute Values|More info...]]

Analytica Cloud Platform

2010-08-13T18:40:13Z

Nkretz: /* C. Application Plan */

=Analytica Web Player=

The Analytica Web Player (AWP) lets anyone view and run an Analytica model via a web browser. It also makes it easy to create web applications with Analytica. Users can open a model, view diagrams and objects, change input variables, and view results as tables and graphs via common web browser, such as Internet Explorer, Firefox, or Safari. AWP runs the Analytica models on a server computer using ADE (the Analytica Decision Engine).

AWP offers several advantages over Analytica Player for distributing models:
* Users can review and run Analytica models without having to download or install any new software on their desktop -- they don't need to consult their IT department. You can invite model reviewers or users simply by emailing them the URL of a model in AWP.
* Model authors can be sure that end users are using the latest version of models and data posted on the server, without having to worry about distributing updates to users individually.
* By using password-protected AWP accounts, you can reduce the possibility that sensitive or proprietary models will be seen by unauthorized people. (You can also use the information hiding features of Analytica Enterprise to hide selected data or formulas.)
* With the Application Subscription to AWP, users can save changes to their models for use in later sessions -- similar to the Analytica Power Player, but not possible with the free Analytica Player.
* Once your organization purchases a subscription, you can make Analytica models accessible to many users, without have to purchase an extra license for each user -- as you would with the Analytica Power Player.

Model authors can upload models into AWP directly from their desktop. Users can save changed models, to revisit them in later sessions. Usually, AWP directories are password protected, so only authorized users can view and use models. But, we also make a free AWP directory available for people who want to share their models openly.

We welcome your comments. Please tell us what you think, including how you would like to use AWP, and any additional features you would like.

== AWP is like the Analytica Player ==

AWP offers almost all the features of desktop Analytica Player. It lets you open a model, view diagrams, object windows, and results as graphs or tables. It lets you change any variable designated as an input, including as an Edit table; but it won't let you change other variables or create new objects.

Soon, like the Power Player, it will let you save a changed model so you can continue with the changed model in a later session.

== How AWP improves on Analytica Player ==

AWP offers several enhancements that are not (yet) available in desktop Analytica:

* When you move the cursor over a node (without clicking), it highlights the node with a light rectangle around it. After a couple of seconds, it shows the description of the node (if it has one) in a popup balloon.
* Like most web applications, you use ''single'' not double clicks to drill down -- for example, to open a module diagram.
* It can display a result table or graph, or edit table, ''embedded'' in a diagram, instead of having to open the result or edit table in a separate window. As model author in desktop Analytica, you simply make the height of the input or output node greater than four times the default size, i.e. 104 pixels (52 nodesize units). When you upload and open the model with AWP, the graph or table appears embedded in its parent diagram, in the rectangle with size and location specified for the node.

== AWP access ==

=== A. Demo and Trial Access ===

* If you haven't seen AWP in action, you can view a few example models in AWP.
** [http://www.analyticawebplayer.com/2.0/Client/AwpClient.aspx?inviteId=3&inviteCode=820186&subName=awp%20demos Play the Rent vs. Buy model in AWP.] The Rent vs. Buy model is an Analytica example model used in the Analytica Tutorial. It's a financial model comparing the Net Present Value of renting vs. buying a house. Model navigation is fairly intuitive. You can switch diagrams by clicking on modules or selecting different modules in the outline tree located on the left hide side of the diagram. Clicking on variable nodes generally displays the node's result. Hover the mouse over a node to see its description. One hint, you can right click on a node and select Object view from the popup menu to view the node's attributes in the object tab, notably the node's definition can be viewed here.
** [http://www.analyticawebplayer.com/2.0/Client/AwpClient.aspx?inviteId=2&inviteCode=784155&subName=awp%20demos Play the Foxes and Hares model in AWP.] The Foxes and Hares model is preditor / prey example mode also used in the Tutorial. When playing this model AWP adds beveled gradients and drop shadows to nodes on the influence diagram. Also, the default tabbed UI has been removed. Node descriptions and definitions can be seen by hovering over a node, and model results are displayed directly on the diagram.
* Sign up for a '''free Trial''' access to AWP at: [http://lumina.com/ana/trialAWP.htm The Analytica Web Player]. Then '''upload your own models''' and see how they work. Explore the features for inviting others to view your models, and for controlling who has access to your models.

=== B. Model Reviewer plan ===

* Post your own models so others can view and run them.
* Send email invites with web link to allow others to view your model.
* Up to 200 user sessions per month
* Each evaluation must complete within 60 CPU seconds.
* $500/month or $5000/year subscription

=== C. Group Subscription Plan ===

Features for Admin (Subscriber)
* No limit to number of models posted.
* Password protect model use.
* Model Customization (ie. change logo, etc.)
* Multiple project directories.
* Can view how many user sessions are still available in month.
* Limit of 250 credits per month.
* Extra AWP credits can be bought for $1/credit in blocks. See below to buy blocks of additional credits.

Features for End Users:
* Database querying.
* Non-anonymous reviewers can save their inputs as a snapshot that can be loaded by that same user later, or reviewed by the subscriber

Price: $250/month or $2000/year

== Other differences ==

===UI Differences===
AWP has a few differences from Analytica in its user interface:

* It only shows one window at a time (plus optionally an outline window on the left)
* A single click on a variable shows its ''result'', as a table or graph, not object window.
* It offers tabs across the top to show the parent diagram, object view, table, or graph for the selected object.
* The expandable Outline view shows only modules, not variables or other objects. You can switch off display of the Outline by [Fred please complete]

===Linked Modules/Libraries===

* If your Analytica model contains linked libraries or linked modules, you can still play it on AWP, but it may require some tweaking.
* The simplest solution is to convert the linked libraries and linked modules into embedded modules and embedded libraries.
** To do this, e.g. for a linked module, bring up the object window for the linked module, then change the class from linked module to an embedded module. Then save the model file, and upload it onto AWP.
** Another way to do this especially if you've got multiple modules/libraries, is to select 'Save a copy in ...' from Analytica's file menu, and in the dialog that appears, check the 'Save everything in one file by embedding linked modules' checkbox. See screenshot below. Note this feature was adding in Anaytica 4.2 and is not available in earlier releases.

[[Image:Save everything in one file.png]]

* Another solution is to upload the linked modules and linked libraries onto the AWP server. In this case, you need to be sure that linked modules and linked libraries are in the same directory as your model fie. So when all the files are uploaded onto the server and the model file is opened, AWP will find and successfully load the linked modules and linked libraries.

== Features of Analytica not available in AWP ==

These features of Analytica Player are not (yet) available in AWP:
* Dialog boxes to change computation and display options, such as the Graph setup, Number format, Diagram and Node styles, Uncertainty Setup, and Preferences dialogs. It uses whatever options and styles you chose in Analytica before uploading the model to AWP.
* Slicer menus for multidimensional results that were introduced in Analytica 4.0. E.g. Color key slicer, Symbol key slicer, Symbol Size Key slicer and bar origin slicer.
* Clickable references.
* It has limited ability to copy and paste a subset of values from and to a table, and no ability to copy diagrams and graphs (except if you use Snagit or a similar application for selecting material to copy).
* No support for 'Check' attribute
* No support for 'MsgBox' function
* Other features which are new in Analytica 4.0/4.1 are being added to AWP. If you see something missing that you would like to have, let us know.

== Features of AWP not available in Analytica ==

AWP offers several options to modify the style of the user interface:

'''Tables and graphs in the diagram'''
* Embed edit tables and result tables and graphs in their parent diagram, so you can see the diagram and results in the Browser window.
* Reserve a space in a diagram to show the edit table, or result table or graph for any node you select in the diagram. This is especially handy for web applications.

'''[[AWP Rendering tables and graphs on the diagram|Click here for more info...]]

'''Using Awp_attrib attribute settings'''

* Don't display the the outline view (the default is on.) The outline is useful to help users navigate around large models, but not needed for small models or when you don't want users to look at all its details.
* Display only the top diagram, and not any submodules -- especially useful for web applications.
* Add bevel and shadows to nodes to make diagrams appear more dramatic.
* Tell AWP not to display things, e.g. don't allow users to save, or restrict model browsing so certain parts of your model are not accessible.

You can control these options with keywords added to a new attribute called '''Awp_attrib'''. See '''[[AWP Attribute Values]]''' for details.

== Enhancement requests==

* Do not show index selectors for tables and graphs. Currently, it only shows the selectors for 2D results, not the slicers for 3rd and higher dimensions anyway. For most applications, the model author selects the best view, and showing these options to end user adds needless complication.

== AWP Attribute ==

When creating a model in Desktop Analytica you can add an attribute Awp_attrib which can be used to specify certain behaviors when the model is played in AWP. [[AWP Attribute Values|More info...]]

Jobs for Analytica experts

2010-07-29T18:41:22Z

Nkretz: /* June 1, 2009. Principal Energy Analyst, Lumina Decision Systems, Los Gatos, CA */

=Employment Opportunities=

==Guidelines==
When adding an employment opportunity to the job listings, please indicate the following:
*Date of Job posting
*Job Title
*Name of Company/Organization
*Location (City, State)

Please provide a brief description of the job requirements in addition to your contact details.

You may edit the sample template below as required:
===Date Title, Company Name, Location===
:: '''Description:'''
:: '''Contact:'''

==Jobs==

=== July 15th, 2010. Energy Market Analyst/Emerging Technologies Consultant, Customized Energy Solutions ===

: '''Description:''' We are looking for a talented and hard-working individual with a minimum of five years experience in the deregulated electric industry to support the electricity market analysis consulting work of Customized Energy Solutions. This position will be largely focused performing analytical project work geared towards evaluations of aspects of the electricity markets. Specifically, the analyses are likely to deal emerging technologies including energy storage technologies, renewable energy products, demand response opportunities, and smart grid initiatives, but will also deal with conventional generation and load modeling. Examples of specific responsibilities include:

:* Lead interactions with energy storage and renewable technology companies to develop projects for market evaluation.
:* Participate in market analysis projects with energy storage and renewable elements, as well as other analysis projects. These projects will require the Consultant to produce reports based on market knowledge, understanding of available technologies, market rule research, pricing research, and other inputs.
:* Assist clients in developing proposals for emerging technology funding from venture capital funds, financial institutions, and / or other funding agencies.
:* Assist financial institutions in due diligence of new technologies based on available market opportunities, potential technology improvements, market rule changes and competition from other solution providers.
:* Participate in conferences / tradeshows for the primary purpose of developing additional business for Customized Energy Solutions.

: '''Preferred experience:'''
:* General knowledge of wholesale electricity markets
:* Some specific understanding of energy storage technology
:* A Masters Degree; preferably in electrical engineering or other engineering field
:* Experience in performing project research
:* Strong analytical reasoning skills
:* Understanding of project financial modeling
:* Experience with statistical analysis & Modeling tools, such as Palisade Decision Tools suite including @Risk and / or Analytica
:* Good written and oral communication skills
:* Ability to be self directed as well as work well with others
:* Experience with standard office software
:* Specific knowledge of the RTO/ISO wholesale markets
:* Data mining experience
:* Database software skills, specifically Microsoft SQL experience
:* Advanced education

Determination of Consultant / Analyst and compensation is commensurate with experience and performance. Benefits include 401K,
profit sharing plan, and comprehensive medical and dental insurance. This opportunity is based out of our center city
Philadelphia, Pennsylvania location with close proximity to public transportation and cultural attractions.

: '''Contact:''' Send your resumes to rahul@ces-ltd.com Rahul Walawalkar Ph.D.,Vice President, Emerging Technologies & Markets

===July 29, 2010, Senior Analyst, Enrich Consulting, San Jose, California===
: '''Description:''' Enrich Consulting builds web-based enterprise tools to enable stage-gate, portfolio management, and project valuation decision making. We use Analytica as a behind-the-scenes number cruncher extraordinaire and are currently looking for experienced Analytica modelers who can support our clients' needs for sophisticated yet easy-to-use financial modeling tools. Below I've included information on one of our openings. For more information please see the 'company' section of our website [http://enrichconsulting.com] --Rich Sonnenblick

:As a Senior Analyst, you will perform a leading role on client engagements, working closely with clients to understand key business issues and translate them into business and financial models.

:'''Other duties:'''
:* Develop comprehensive financial models to evaluate individual R&D initiatives and R&D portfolios
:* Responsible for driving implementations of the Enrich Portfolio System (EPS) software
:* Help clients understand and incorporate market and development risk in their analysis of strategic alternatives
:* Conduct training sessions on the EPS and decision analysis
:* Assist clients with the development of their portfolio processes
:* Develop and summarize insights for communicating to clients
:* Help to develop new approaches to problems and expand applications to new industries
:'''Qualifications:'''
:* BA, MS, or PhD in Operations Research, Applied Math, Physics, Engineering, Economics or related field -OR-
:* MBA with a concentration in quantitative business forecasting, decision making, or market analysis
:* 3-6 years work experience in a quantitative position in the management consulting, pharmaceutical, biotechnology, and/or high-technology industries
:* Demonstrated excellent analytical and computer skills
:* Proficiency modeling with Excel spreadsheets
:* Very strong oral and written communication skills
:* Enjoy explaining/teaching concepts to others
:* Desire for a high level of responsibility and ability to work with minimal supervision
:* Understanding of financial statements and financial analysis
:* Ability to quickly learn new skills and methodologies
:* Desire to work in a team-oriented, informal, small company environment
:* Willingness to travel up to 30%
:* Willingness to relocate to the San Francisco / South Bay area
:'''Preferred:'''
:* Experience creating financial models for R&D valuations and/or R&D portfolio management
:* Demonstrated leadership skills in multiple settings and ability to manage many priorities and multi-task
:* Familiarity with decision modeling software such as Analytica, DPL, Crystal Ball, or enterprise data analysis applications
:* Experience deploying analytic applications on the web
:* Knowledge of the pharmaceutical, biotechnology, and/or high-tech industries

: '''Contact:''' Please send your resume and a cover letter describing your qualifications for the position to [mailto:careers@enrichconsulting.com]

Analytica User Group

2010-03-18T20:39:47Z

Nkretz: /* Analytica User Group:Large Sample Library */

Analytica User Group

2010-03-18T20:38:53Z

Nkretz: /* The Large Sample Library */

Analytica User Group

2010-03-18T20:37:42Z

Nkretz: /* The Large Sample Library */

Analytica User Group

2010-03-18T20:34:23Z

Nkretz: /* Analytica User Group: Large Sample Library */

[[/Past Topics|Past Topics]] >>

The Analytica User Group is a way for Analytica users to support each other by sharing tips and function libraries. It includes a webinar series with tutorials and demonstrations on key Analytica features.

= Webinar Series =

Analytica User Webinars are a great way to learn about key Analytica features and modeling skills. They are live demos, using Citrix Gotowebinar, and VOIP or your telephone, at your choice. Questions, comments, and tangents are welcome.

There are webinars most weeks, usually on Thursdays at 10am PST (1pm EST). Seats are limited. To sign up for a particular webinar, see "How to Attend" below. Presentations may last anywhere from 20 to 90 minutes (with an estimate provided upfront).

If you missed a User Webinar, don't despair. We usually records them, including audio and screensharing. So, you can replay them any time. See below for the full list.

== Schedule of Upcoming Webinars ==

=== [[Model building by mouse]] ===

'''Date and Time:''' (TBD)

'''Presenter''': (TBD)

'''Abstract'''

See [[Model building by mouse]] for the concept. The talk will also cover the [[TemplateInput]] and [[TemplateOutput]] attributes new to 4.2 that make template modules possible, and thus expand the potential power and applicability of this modeling technique.

=== Saving Memory: Controlling when results are (and are not) cached ===

'''Date and Time:''' (TBD)

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

When a variable is computed, Analytica caches the result (i.e., keeps a copy around) so that when the same result is needed later, it does not need to be recomputed. Large array-valued results can end up consuming large amounts of memory, and lead to annoyances such as running out of available memory. There are instances where it isn't really necessary to cache a result -- either it will never be needed a second time, or it isn't a big deal to recompute it, or it is an exact copy of its parent (perhaps because only the result graph settings are different), so that by configuring that variable to not cache its result, or to release its cache as soon as all its children are computed, memory space can be recovered, perhaps allowing larger simulations to be conducted.

Analytica 4.2 introduces a new feature allowing you to configure when results are (or are not) cached. I'll demonstrate how to utilize this feature, and discuss limitations, such as situations where it would be a very bad idea not to cache.

If time allows (it probably will), I'll also throw in two other small memory-related controls new in 4.2. The [[WhatIf]]-style functions ([[WhatIf]], [[WhatIfAll]], [[Dydx]], [[Elasticity]], [[NlpDefine]]), i.e., functions that compute hypotheticals, have been modified in 4.2 to preserve previously computed values of other variables. This is a nice feature, but can impact tight-memory cases. I'll explain this change and how to avoid the extra memory consumption where appropriate. Second, I'll also show how Analytica's maximum working set size can be configured (on some operating systems), which can be used to keep other applications on your computer highly responsive even when large memory-intensive computations are churning away.

In Analytica 64-bit, you are less likely to encounter the problem of running out of memory, but you still may encounter performance slowdown from huge memory utilization. Some large models, requiring extensive memory resources, run smoothly even when requiring far more memory than there is RAM available, while other result in extensive ''thrashing'', taking very long to evaluate. I've found that differentiating factor has to do with the size of individual arrays within the model. Thrashing tends to occur when a single array consumes more than about 1/3 the available RAM, while if all arrays in the model remain small relative to available RAM, huge memory evaluations often run very smoothly. I'll relate these observations, and what that means with respect to sample size.

This talk is appropriate for advanced Analytica modelers.

=== The [[Dynamic]] Function and Dynamic Loops ===

'''Date and Time:''' (TBD)

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

The [[Dynamic]] function in Analytica 4.2 is a bit more flexible than previously. It is now possible for [[User-Defined Functions]] to be part of a dynamic loop, and there are cases where it is now possible to operate over the [[Time]] index where previous it was not. I'll discuss how dynamic models are evaluated, and how that differs from non-dynamic models, and cover some tricks for tracing evaluation and debugging complex dynamic models (which can often be challenging to debug). I'll also introduce how [[Dynamic]] can be used on an index other than the built-in [[Time]] index.

=== [[IntraTable]]s ===

'''Date and Time:''' (TBD)

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

An [[IntraTable]] is a variation of an edit [[Table]], where (like [[Table]]s), the cells may contain expressions, but unlike [[Table]]s, the expressions in each cell can refer to values of other cells. This may (quite appropriately) conjure up nightmares of spreadsheet hell. While this isn't something you'd want to use nonchalantly, there are cases when the ability to reference other cells within the same table comes with its benefits. Because the [[IntraTable]] allows any pattern of references (it doesn't have to be strictly left-to-right, for example), as long as a cell-cycle isn't created, it is possible to encode certain recurrences that would otherwise be quite difficult to encode.

== How to Attend ==

To attend, you need to sign up by contacting Lumina at mailto:webinars@lumina.com. Please sign up at least a day prior. Attendance is limited, so please don't sign up unless you sincerely intend to attend.

These Webinars are FREE to users who have an up-to-date Support for Analytica. If you are unsure, check with mailto:sales@lumina. For those without current support, the fee is US$50.

== How to be a Presenter ==

Being a presenter at an Analytica webinar provides an opportunity to make others in the Analytica community aware of your successes or capabilities. Consultants may find this an opportunity for exposure to others with particular modeling needs. Also, if you are an Analytica aficionado, this is a great opportunity to help others.

If you would like to be a presenter, submit your proposed topic to webinars@lumina.com and possible presentation times (include the time zone). We will schedule the GotoMeeting conference (you do not need a gotoMeeting subscription yourself) and we will make you presenter during the session, allowing you to share your screen while you talk. You will most likely make use of Power Point and a running Analytica during your presentation.

== Analytica User Group: Large Sample Library ==
[http://lumina.com/wiki/index.php/Analytica_User_Group/Past_Topics#The_Large_Sample_Library]

== [[Analytica User Group/Past Topics|Archive of Past Topics]] ==

Visit the [[Analytica User Group/Past Topics|Archive of Past Topics]] for abstracts, example models, and recordings of previous Analytica User Group webinars. Previous topics that have been presented include:

* Guidelines for Model Transparency
* Automated Monitoring and Failure Detection
* Principle Component Analysis (PCA)
* Internal Rate of Return ([[IRR]]) and Modified Internal Rate of Return ([[MIRR]])
* Bond Portfolio Analysis
* Net Present Value ([[NPV]])
* The Analytica Wiki, and How to Contribute
* Data Center Capacity Planning
* Modeling the Precision Strike Process
* The Performance Profiler
* Variable Stiffness Cubic Splines
* Introduction to [[DetermTable]]s
* Importance Sampling (Bayesian posteriors)
* Importance Sampling (rare events)
* Statistical Functions
* [[Table Splicing]]
* Analytica Web Player
* [[SubTable|SubTables]]
* Creating Custom Distribution Functions
* Using the Check attribute to validate inputs and results
* The [[Dynamic]] Function
* [[Logistic Regression]]
* Creating Control Panels
* Statistical Functions
* Creating Scatter Plots
* Using [[Regression]]
* New Functions for Reading Directly from an Excel File
* Advanced Tornado Charts -- when inputs are Array-Valued
* Using the Analytica Decision Engine (ADE) from ASP.NET
* OLE Linking
* Tornado Charts
* Correlated and Multivariate Distributions
* Assessment of Probability Distributions
* Graph Style Templates
* Sneak preview of Analytica Web Publisher
* Querying an OLAP server
* Querying an ODBC relational database
* [[Self-Indexed Arrays|Self-Indexes]], Lists and [[Implicit Dimensions]]
* Flattening and Unflattening of Arrays
* Introduction to Arrays and Array Abstraction
* [[Local Indexes]]
* The [[Iterate]] Function
* The [[Using_References|Reference and Dereference Operators]]
* Modeling Utility Tariffs in Analytica
* Modeling Energy Efficiency in Large Data Centers
* Calling External Applications
* Introduction to Linear and Quadratic Programming
* Non-Linear Optimization
* Writing [[User-Defined Functions]]
* Modeling Markov Processes in Analytica
* Manipulating Dates in Analytica
* Button Scripting
* Manipulating Indexes and Arrays in Analytica Expressions
* Edit Table Enhancements in Analytica 4.0
* [[Handle]]s and [[Meta-Inference]]

== Potential future topics ==

If you would like to see a webinar on a given topic, please feel free to add it here. If you see a topic listed and would like to be a presenter, let us know.

* Statistical hypothesis testing.
** Standard textbook tests
** Computing p-values using Monte Carlo for complex and non-standard statistical models.

* Time-series analysis

* Using the interpolation functions, [[LinearInterp]], [[CubicInterp]], [[StepInterp]].

* Numeric precision - numeric round-off, underflow, etc., why they happen, what to do about it.

* Net Present Value and Internal Rate of Return -- introduction to the use of these commonly used metrics for quantifying decision quality.

* Using the ''Performance Profiler'' to understand where your model consumes time and memory resources.

* Producing graphs from ADE: Including how to serve graphs from web pages.

* Intracacies of the Domain attribute

* Mastering Array Abstraction

* Writing User-Defined Distribution functions.

* Getting data into Analytica.

* [[DetermTable]]s

* Large-scale sampling. Techniques when memory limitations constrain sampleSize.

* Creating User-Defined functions. Review of [[Function Parameter Qualifiers]].

* Understanding [[Evaluation Contexts]]

* Bayesian Inference

* Sensitivity Analysis.

* Dynamic Programming

* Approximate and Stochastic Dynamic Programming

== User Survey Results ==

During the first week of September, we sent out a survey to people who had attended webinars so far. Please continue providing us with feedback. Here is some feedback to date:

Level of difficult and speed:

So far, of those who answered this question, 25% say "a bit too easy", 25% say "too hard/fast", and 50% say "just right".

Topics requested for future webinars:

* How to run multiple iterations, e.g. 100 iterations with uncertainty sample of 1000.

* Sampling for rare events.

* Using the lognormal function.

* Financial modeling

* New array functionality (subtables, choices in tables)

* Optimizer !!!

* Dynamic models (twice requested)

* Choice of distributions

* Re-sampling and radomize methods and uncertainty sample size

* Tricks for sensitivity analysis

* Input and output nodes

* Importance analysis

= The Analytica Wiki =

The [[Analytica Wiki ]] contains many resources, including in-depth reference materials, relevant articles, example models, tutorials, etc., to help users master Analytica and find what they need. Even better, Analytica users can contribute! You can upload your own models, articles, expand on or correct materials that are there, etc., for the benefit of the entire Analytica community.

Analytica User Group

2010-03-18T20:33:29Z

Nkretz: /* Archive of Past Topics */

Jobs for Analytica experts

2008-12-09T20:01:37Z

Nkretz: /* Jobs */

=Employment Opportunities=

==Guidelines==
When adding an employment opportunity to the job queue, please indicate the following:
*Date of Job posting
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Please provide a brief description of the job requirements in addition to your contact details.

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# '''Date Title, Company Name, Location'''
:: '''Description:''' Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

==Jobs==
1. '''Date Title, Company Name, Location'''
: '''Description:'''

Jobs for Analytica experts

2008-12-09T20:00:42Z

Nkretz: /* Jobs */

=Employment Opportunities=

==Guidelines==
When adding an employment opportunity to the job queue, please indicate the following:
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==Jobs==
# '''Date Title, Company Name, Location'''
:: '''Description:'''
## '''Date Title, Company Name, Location'''
:: '''Description:'''

Jobs for Analytica experts

2008-12-09T20:00:30Z

Nkretz: /* Jobs */

=Employment Opportunities=

==Guidelines==
When adding an employment opportunity to the job queue, please indicate the following:
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==Jobs==
# '''Date Title, Company Name, Location'''
:: '''Description:'''
# '''Date Title, Company Name, Location'''
:: '''Description:'''

Jobs for Analytica experts

2008-12-09T20:00:18Z

Nkretz: /* Jobs */

=Employment Opportunities=

==Guidelines==
When adding an employment opportunity to the job queue, please indicate the following:
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Please provide a brief description of the job requirements in addition to your contact details.

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==Jobs==
# '''Date Title, Company Name, Location'''
:: '''Description:'''

Jobs for Analytica experts

2008-12-09T20:00:09Z

Nkretz: /* Guidelines */

Jobs for Analytica experts

2008-12-09T19:59:45Z

Nkretz: /* Guidelines */

Jobs for Analytica experts

2008-12-09T19:58:29Z

Nkretz: /* Guidelines */

=Employment Opportunities=

==Guidelines==
When adding an employment opportunity to the job queue, please indicate the following:
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==Jobs==

Jobs for Analytica experts

2008-12-09T19:57:45Z

Nkretz: /* Guidelines */

=Employment Opportunities=

==Guidelines==
When adding an employment opportunity to the job queue, please indicate the following:
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Please provide a brief description of the job requirements in addition to your contact details.

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# '''12/08/2008 Wiki Editor, Lumina Decision Systems, Inc. Los Gatos, CA'''
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==Jobs==

Jobs for Analytica experts

2008-12-09T19:56:05Z

Nkretz: /* Guidelines */

=Employment Opportunities=

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When adding an employment opportunity to the job queue, please indicate the following:
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You may edit the sample template below as required:
# '''12/08/2008 Wiki Editor, Lumina Decision Systems, Inc. Los Gatos, CA'''
:: Description: Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

==Jobs==

Jobs for Analytica experts

2008-12-09T19:43:45Z

Nkretz:

Analytica Docs

2008-12-09T19:34:34Z

Nkretz:

Welcome to the Analytica Wiki! It is a shared resource created for and by the community of Analytica users. It includes FAQs, tips, examples, shared libraries, and details describing Analytica functions expanding on the User Guide.

Because it's a wiki, you can edit and add to it, as well as read it -- like Wikipedia. If you are an Analytica user with current support, you should have password access to read and edit it. If you don't, please email info@lumina.com to get a password.

If you can't find the information you want -- or can't understand it -- please add your question or comment, perhaps to the relevant ''discussion'' page. If you have information, tips, examples, or libraries you want to share, your contributions are welcome.

{| border="1" align="center"
|
{| border="0"
! '''Latest Analytica Release News!'''
|-
!''12 Sept 2008''
|-
| [[Patch Release 4.1.2 is now available for Analytica and ADE]]
|}
|}

* [[What's new in Analytica 4.1?]]: Release on April 1, 2008, this page lists enhancements since the 4.0 release.
** [[What's fixed in release 4.1.2?]]: Patch released on 12 Sept 2008 fixes various bugs.
** [[What's new in Analytica 4.0?]]: A list of new features and functions in Analytica and ADE 4.0, released Nov 30, 2007, with links to more detailed explanations.

* [[Analytica Web Player]]: Lumina's newest offering, the AWP subscription service.


* [[Analytica Modelers Guide]]: Hints and examples.

* [[Analytica Reference]]: The real details, but only on a few features at present.

* [[Analytica Libraries and Templates]] and [[Example Models]]: New! Grant Exclusion model.

* [[Analytica User FAQs]]: Frequently Asked Questions and support queries.

* [[Analytica in the Classroom]]: Resources for using Analytica for teaching

* [[Analytica User Group]] : Webinars for active users.
** [[Analytica User Group/Past Topics|Archives of previous webinars]]

* [[Enhancement Requests for Analytica]]: Here's what else I think Analytica should do.

* [[Comments and suggestions for Analytica wiki]]: Here's how we can improve this Wiki.

* [[Job Postings]]: Jobs available for Analytica users.

* [[How to edit Analytica wiki]]: Tips on how to edit and add to these documents.

* List of [[Special:Uncategorizedcategories|Top Level Categories]] or [[Special:Categories|All Categories]] (indexes into the Analytica Wiki site)

[[Image:Analytica logo tagline 500px.png]]