Difference between revisions of "Example Models and Libraries - Table"

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|[[media:French-English.ana|French-English.ana]]
 
|[[media:French-English.ana|French-English.ana]]
 
|multi-lingual models
 
|multi-lingual models
|<div style="text-align: left;">Maintains a single influence diagram with Title and Description attributes in both English and French.  With the change of a pull-down, the influence diagram and all object descriptions are instantly reflected in the language of choice.<br /><br />
+
|<div style="text-align: left;">Maintains a single influence diagram with Title and Description attributes in both English and French.</div>
If you change a title or description while viewing English, and then change to French, the change you made will become the English-language version of the description.  Similarly if you make a change while viewing French.</div>
 
 
|
 
|
 
|[[Multi-lingual Influence Diagram]]
 
|[[Multi-lingual Influence Diagram]]
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|[[media:Parsing XML example.ana|Parsing XML example.ana]]
 
|[[media:Parsing XML example.ana|Parsing XML example.ana]]
 
|data extraction, xml, DOM parsing
 
|data extraction, xml, DOM parsing
|<div style="text-align: left;">Suppose you receive data in an XML format that you want to read into your model. This example demonstrates two methods for extracting data: Using a full XML DOM parser, or using regular expressions. The first method fully parses the XML structure, the second simply finds the data of interest by matching patterns, which can be easier for very simple data structures (as is often the case).</div>
+
|<div style="text-align: left;">Demonstrates two methods for extracting data: Using a full XML DOM parser, or using regular expressions.</div>
 
|
 
|
 
|[[Extracting Data from an XML file]]
 
|[[Extracting Data from an XML file]]
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|[[media:Vector Math.ana|Vector Math.ana]]
 
|[[media:Vector Math.ana|Vector Math.ana]]
 
|
 
|
|<div style="text-align: left;">Functions used for computing geospatial coordinates and distances. Includes:<br />
+
|<div style="text-align: left;">Functions used for computing geospatial coordinates and distances.</div>
* A cross product of vectors function
 
* Functions to conversion between spherical and Cartesian coordinates in 3-D
 
* Functions to compute bearings from one latitude-longitude point to another
 
* Functions for finding distance between two latitude-longitude points along the great circle.
 
* Functions for finding the intersection of two great circles</div>
 
 
|geospatial analysis, GIS, vector analysis
 
|geospatial analysis, GIS, vector analysis
 
|[[Vector Math]]
 
|[[Vector Math]]
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|[[media:Total Allowable Removal model with Optimizer.ana | Total Allowable w Optimizer.ana]] or<br />[[media:Total Allowable Removal model w StepInterp.ana|Total Allowable w StepInterp.ana]] for those without Optimizer
 
|[[media:Total Allowable Removal model with Optimizer.ana | Total Allowable w Optimizer.ana]] or<br />[[media:Total Allowable Removal model w StepInterp.ana|Total Allowable w StepInterp.ana]] for those without Optimizer
 
|
 
|
|<div style="text-align: left;">The problem applies to any population of fish or animal whose dynamics are poorly known but can be summarized in a simple model:<br /><br />
+
|<div style="text-align: left;">Determines how many fish or animals can be caught (landed) annually so that the probability of the population declining X%  in Y years (decline threshold) is less than Z% (risk tolerance).</div>
:<code>N_t + 1 = N_t*Lambda - landed catch*(1 + loss rate)</code>
 
 
 
where «N_t» is the population size (number of individuals) at time ''t'', «N_t+1» is the population size at time ''t + 1'', «Lambda» is the intrinsic rate of increase and the «loss rate» is the percentage of fish or animals killed but not retrieved relative to the «landed catch», or catch secured.<br /><br />
 
The question here is to determine how many fish or animals can be caught (landed) annually so that the probability of the population declining X%  in Y years (decline threshold) is less than Z% (risk tolerance).  <br /><br />
 
Two models are available for download.  One uses the Optimizer ([[NlpDefine]]) to find the maximum landed catch at the risk tolerance level for the given decline threshold. The other (for those using a version of Analytica without Optimizer) uses [[StepInterp]] in an iterative way to get that maximum landed catch.</div>
 
 
|population analysis, dynamic models, optimization analysis
 
|population analysis, dynamic models, optimization analysis
 
|[[Total Allowable Harvest]]
 
|[[Total Allowable Harvest]]
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|[[media:Cereal Formulation1.ana|Cereal Formulation1.ana]]
 
|[[media:Cereal Formulation1.ana|Cereal Formulation1.ana]]
 
|product formulation, cereal formulation
 
|product formulation, cereal formulation
|<div style="text-align: left;">A cereal formulation model<br /><br />
+
|<div style="text-align: left;">Cereal formulation/discrete mixed integer model that chooses product formulations to minimize total ingredient costs.</div>
A discrete mixed integer model that chooses product formulations to minimize total ingredient costs. This could be an NSP but it uses two methods to linearize:
 
1) Decision variable is constructed as a constrained Boolean array
 
2) Prices are defined as piecewise linear curves</div>
 
 
|
 
|
 
|[[Linearizing a discrete NSP]]
 
|[[Linearizing a discrete NSP]]
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| Model
 
| Model
 
|[[media:Neural-Network.ana|Neural Network.ana]]
 
|[[media:Neural-Network.ana|Neural Network.ana]]
|feed-forward neural networks
+
|feed-forward neural networks, non-linear regression
|<div style="text-align: left;">A feed-forward neural network can be trained (fit to training data) using the Analytica Optimizer.  This is essentially an example of non-linear regression.  This model contains four sample data sets, and is set up to train a 2-layer feedforward sigmoid network to "learn" the concept represented by the data set(s), and then test how well it does across examples not appearing in the training set.
+
|<div style="text-align: left;">Models set up to train a 2-layer feedforward sigmoid network to "learn" the concept represented by the data set(s), and then test how well it does across examples not appearing in the training set.</div>
 
 
Developed during the Analytica User Group Webinar of 21-Apr-2011 -- see the [[Analytica_User_Group/Past_Topics#Neural_Networks|webinar recording]].</div>
 
 
|optimization analysis
 
|optimization analysis
 
|[[Neural Network]]
 
|[[Neural Network]]
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|[[media:Earthquake expenses.ana|Earthquake expenses.ana]]
 
|[[media:Earthquake expenses.ana|Earthquake expenses.ana]]
 
|
 
|
|<div style="text-align: left;">An example of risk analysis with time-dependence and costs shifted over time.<br /><br />
+
|<div style="text-align: left;">An example of risk analysis with time-dependence and costs shifted over time.</div>
Certain organizations (insurance companies, large companies, governments) incur expenses following earthquakes.  This simplified demo model can be used to answer questions such as:<br />
 
* What is the probability of more than one quake in a specific 10 year period.
 
* What is the probability that in my time window my costs exceed $X?
 
 
 
<br />Assumptions in this model:
 
* Earthquakes are Poisson events with mean rate of once every 10 years.
 
* Damage caused by such quake is lognormally distributed, with mean $10M adn stddev of $6M.
 
* Cost of damage gets incurred over the period of a year from the date of the quake as equipment is replaced and buildings are repaired over time:  20% in 1st quarter after quake, 50% in 2nd quarter, 20% in 3rd quarter, 10% in 4th quarter.</div>
 
 
|risk analysis, cost analysis
 
|risk analysis, cost analysis
 
|[[Earthquake Expenses]]
 
|[[Earthquake Expenses]]
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|'''Best used with Analytica Optimizer'''<br />[[media:Loan policy selection.ANA|Loan policy selection.ana]]
 
|'''Best used with Analytica Optimizer'''<br />[[media:Loan policy selection.ANA|Loan policy selection.ana]]
 
|creditworthiness, credit rating, default risk
 
|creditworthiness, credit rating, default risk
|<div style="text-align: left;">A lender has a large pool of money to loan, but needs to decide what credit rating threshold to require and what interest rate (above prime) to charge.  The optimal value is determined by market forces (competing lenders) and by the probability that the borrower defaults on the loan, which is a function of the economy and borrower's credit rating.  The model can be used without the Analytica optimizer, in which case you can explore the decision space manually or use a parametric analysis to find the near optimal solution.  Those with Analytica Optimizer can find the optimal solution (more quickly) using an [[NlpDefine|NLP]] search.</div>
+
|<div style="text-align: left;">Helps a lender decide optimal credit rating threshold to require and what interest rate (above prime) to charge.</div>
 
|risk analysis
 
|risk analysis
 
|[[Loan Policy Selection]]
 
|[[Loan Policy Selection]]
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|[[media:Hubbard and Seiersen cyberrisk.ana|Hubbard_and_Seiersen_cyberrisk.ana]]
 
|[[media:Hubbard and Seiersen cyberrisk.ana|Hubbard_and_Seiersen_cyberrisk.ana]]
 
|cybersecurity risk
 
|cybersecurity risk
|<div style="text-align: left;">The model simulates loss exceedance curves for a set of cybersecurity events, the likelihood and probabilistic monetary impact of which have been characterized by system experts. The goal of the model is assess the impact of mitigation measures, by comparing the residual risk curve to the inherent risk curve (defined as risk without any mitigation measures) and to the risk tolerance curve. This is a translation of a model built by Douglas Hubbard and Richard Seiersen which they describe in their book [https://www.howtomeasureanything.com/cybersecurity/about-the-book/ How to Measure Anything in Cybersecurity Risk], and which they make available [https://www.howtomeasureanything.com/cybersecurity/ here].</div>
+
|<div style="text-align: left;">Simulates loss exceedance curves for a set of cybersecurity events, the likelihood and probabilistic monetary impact of which have been characterized by system experts.</div>
 
|loss exceedance curve, simulation
 
|loss exceedance curve, simulation
 
|[[Inherent and Residual Risk Simulation]]
 
|[[Inherent and Residual Risk Simulation]]
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|[[media:Red State Blue State plot.ana]]
 
|[[media:Red State Blue State plot.ana]]
 
|map, states
 
|map, states
|<div style="text-align: left;">This example contains the shape outlines for each of the 50 US states, along with a graph that uses color to depict something that varies by state (historical political party leaning). You may find the shape data useful for your own plots. In addition, it demonstrates the polygon fill feature that is new in [[Analytica 5.2]].</div>
+
|<div style="text-align: left;">Example containing the shape outlines for each of the 50 US states, along with a graph that uses color to depict something that varies by state.</div>
 
|graphing
 
|graphing
 
|[[Red or blue state]]
 
|[[Red or blue state]]
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|[[media:COVID Model 2020--03-25.ana|COVID Model 2020--03-25.ana]]
 
|[[media:COVID Model 2020--03-25.ana|COVID Model 2020--03-25.ana]]
 
|covid, covid-19, coronavirus, corona, epidemic
 
|covid, covid-19, coronavirus, corona, epidemic
|<div style="text-align: left;">A systems dynamics style SICR model of the COVID-19 outbreak within the state of Colorado. It simulates the progression of the outbreak into the future, examining the expected impact on ventilator (compared to levels available), forecasts number of sick and number of deaths, and also the risk reduction that a "lock down" has based on the date of the start of the lock down and the amount of reduction in social interaction. [https://lumidyne-test-site.webflow.io/the-energy-modeler/covid-19 A Lumidyne blog article] describes the model and conclusions ascertained from it.</div>
+
|<div style="text-align: left;">A systems dynamics style SICR model of the COVID-19 outbreak within the state of Colorado.</div>
 
|
 
|
 
|[[COVID-19 State Simulator, a Systems Dynamics approach]]
 
|[[COVID-19 State Simulator, a Systems Dynamics approach]]
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| Model
 
| Model
 
|[[Media:Corona Markov.ana|Corona Markov.ana]]
 
|[[Media:Corona Markov.ana|Corona Markov.ana]]
|covid, covid-19, coronavirus, corona, epidemic
+
|covid, covid-19, coronavirus, corona, epidemic, sensitivity analyses
|<div style="text-align: left;">Used to explore the progression of the COVID-19 coronavirus epidemic in the US, and to explore the effects of different levels of social isolation. It also includes sensitivity analyses. [https://analytica.com/how-social-isolation-impacts-covid-19-spread-in-us-a-markov-model-approach/ A blog article] showcases this model.</div>
+
|<div style="text-align: left;">Explores the progression of the COVID-19 coronavirus epidemic in the US, and to explore the effects of different levels of social isolation.</div>
 
|
 
|
 
|[[How social isolation impacts COVID-19 spread in the US - A Markov model approach]]
 
|[[How social isolation impacts COVID-19 spread in the US - A Markov model approach]]
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|[[Media:Modelo Epidemiologoco para el Covid-19 con cuarentena.ana|Modelo Epidemiológoco para el Covid-19 con cuarentena.ana]]
 
|[[Media:Modelo Epidemiologoco para el Covid-19 con cuarentena.ana|Modelo Epidemiológoco para el Covid-19 con cuarentena.ana]]
 
|covid, covid-19, coronavirus, corona, epidemic
 
|covid, covid-19, coronavirus, corona, epidemic
|<div style="text-align: left;">Un modelo en cadena de Markov del impacto previsto de la enfermedad coronavirus COVID-19 en el Perú, y del impacto del aislamiento social.  Consulte el artículo [https://www.linkedin.com/posts/jorgemuroarbulu_este-estudio-est%C3%A1-adaptado-a-la-realidad-activity-6650119971621912576-yRbh/ Aislamiento Social y Propagación COVID-19]  para detalles.<br /><br />
+
|<div style="text-align: left;">Un modelo en cadena de Markov del impacto previsto de la enfermedad coronavirus COVID-19 en el Perú, y del impacto del aislamiento social.</div>
An adaptation and extension of Robert D. Brown's Markov Model (the previous example) to the country of Perú, translated into Spanish.</div>
 
 
|
 
|
 
|[[Epidemiological model of COVID-19 for Perú, en español]]
 
|[[Epidemiological model of COVID-19 for Perú, en español]]
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|[[media:COVID-19_Triangle_Suppression.ana|COVID-19 Triangle Suppression.ana]]
 
|[[media:COVID-19_Triangle_Suppression.ana|COVID-19 Triangle Suppression.ana]]
 
|covid, covid-19, coronavirus, corona, epidemic
 
|covid, covid-19, coronavirus, corona, epidemic
|<div style="text-align: left;">A novel approach to modeling the progression of the COVID-19 pandemic in the US, and understanding the amount of time that is required for lock down measures when a suppression strategy is adopted. This model is features in the blog article [https://lumina.com/forecast-update-us-deaths-from-covid-19-coronavirus-in-2020/ Suppression strategy and update forecast for US deaths from COVID-19 Coronavirus in 2020] on the Analytica blog.</div>
+
|<div style="text-align: left;">Models progression of the COVID-19 pandemic in the US, understanding the amount of time that is required for lock down measures when a suppression strategy is adopted.</div>
 
|
 
|
 
|[[A Triangle Suppression model of COVID-19]]
 
|[[A Triangle Suppression model of COVID-19]]
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|[[Media:Simple COVID-19.ana|Simple COVID-19.ana]]
 
|[[Media:Simple COVID-19.ana|Simple COVID-19.ana]]
 
|covid, covid-19, coronavirus, corona, epidemic
 
|covid, covid-19, coronavirus, corona, epidemic
|<div style="text-align: left;">Used to explore possible COVID-19 Coronavirus scenarios from the beginning of March, 2020 through the end of 2020 in the US. The US is modeled as a closed system, which people classified as being in one of the progressive stages: Susceptible, Incubating, Contagious or Recovered. Deaths occur only from the Contagious stage. There is no compartimentalization such as by age or geography.</div>
+
|<div style="text-align: left;">Explores possible COVID-19 Coronavirus scenarios from the beginning of March, 2020 through the end of 2020 in the US.</div>
 
|
 
|
 
|[[COVID-19 Coronavirus SICR progression for 2020]]
 
|[[COVID-19 Coronavirus SICR progression for 2020]]
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|[[Media:US COVID-19 Data.ana|US COVID-19 Data.ana]]
 
|[[Media:US COVID-19 Data.ana|US COVID-19 Data.ana]]
 
|covid, covid-19, coronavirus, corona, epidemic, death, infection
 
|covid, covid-19, coronavirus, corona, epidemic, death, infection
|<div style="text-align: left;">The [https://github.com/nytimes/covid-19-data New York Times has made data available] to researchers on the number of reported cases and deaths in each US county, and state-wide, on each day the pandemic. This model reads in these files and transforms them into a form that is convenient to work with in Analytica. <br /><br />
+
|<div style="text-align: left;">Reads in [https://github.com/nytimes/covid-19-data COVID-19 data from the New York Times] and transforms it into a form that is convenient to work with in Analytica.</div>
'''Requires''': You'll need to install GIT and then clone the NYT repository. The Description of the model gives instructions for getting set up.  You'll also need to have the Analytica Enterprise or Optimizer edition.</div>
 
 
|
 
|
 
|[[COVID-19 Case and Death data for US states and counties]]
 
|[[COVID-19 Case and Death data for US states and counties]]
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|[[Media:Voluntary vs mandatory testing.ana|Voluntary vs mandatory testing.ana]]
 
|[[Media:Voluntary vs mandatory testing.ana|Voluntary vs mandatory testing.ana]]
 
|covid, covid-19, coronavirus, corona, epidemic
 
|covid, covid-19, coronavirus, corona, epidemic
|<div style="text-align: left;">A Navy wants to compare two COVID-19 testing policies. In the first, all crew members must take a COVID-19 test before boarding a ship, and those with a positive test cannot board. In the second policy, testing is encouraged but voluntary -- each sailor has an option of being tested before boarding. This model computes the rate of infection among those allowed to board under the two scenarios, based on prevalence rates, test accuracies and voluntary testing rates. It also examines the probability of achieving zero infections on board, and the sensitivity of the results to input parameter estimates.  [https://analytica.com/voluntary-vs-mandatory-testing-for-naval-crew-selection This model is described in a blog posting].</div>
+
|<div style="text-align: left;">Computes the rate of infection in scenarios when COVID-19 testing is required/optional,based on prevalence rates, test accuracies and voluntary testing rates.</div>
 
|
 
|
 
|[[Mandatory vs Voluntary testing policies]]
 
|[[Mandatory vs Voluntary testing policies]]

Revision as of 08:10, 17 January 2023



This page lists example models and libraries. You can download them from here or (in some cases) link to a page with more details. Feel free to include and upload your own models and libraries.

Model/Library Download Domain Description Methods For more
Model Marginal abatement home heating.ana carbon price, energy efficiency, climate policy
Shows how to set up a Marginal Abatement graph in Analytica.
graph methods, optimal allocation, budget constraint Marginal Abatement Graph
Model Solar Panel Analysis.ana renewable energy, photovoltaics, tax credits
Explores whether it would it be cost effective to install solar panels on the roof of a house in San Jose, California.
net present value, internal rate of return, agile modeling Solar Panel Analysis
Model Items within budget.ana
Given a set of items, with a priority and a cost for each, selects out the highest priority items that fit within the fixed budget.
Items within Budget function
Model Grant exclusion.ana business analysis
Tests a hypothesis about the distribution of an attribute of the marginal rejectee of a grant program, given the relevance of that attribute to award of the grant.
Grant Exclusion Model
Model Project Priorities 5 0.ana business models
Evaluates a set of R&D projects (including uncertain R&D costs/revenues), uses multiattribute analysis to compare projects & generates the best portfolio given a R&D budget.
cost analysis, net present value (NPV), uncertainty analysis Project Planner
Model Steel and aluminum tariff model.ana
Estimate of the net impact of the 2018 import tariffs on steel and aluminum on the US trade deficit.
Steel and Aluminum import tariff impact on US trade deficit
Model Tax bracket interpolation 2021.ana
Computes amount of tax due from taxable income for a 2017 US Federal tax return.
Tax bracket interpolation
Model Feasible Sampler.ana feasibility
Implements a button that will sample a collection of chance variables, then reset the sample size and keep only those sample points that are "feasible".
Model Cross-validation example.ana
Fits a non-linear kernel function to the residual error, and uses cross-validation to determine how many kernel functions should be used.
cross-validation, overfitting, non-linear kernel functions Cross-Validation / Fitting Kernel Functions to Data
Model Bootstrapping.ana
Bootstrapping; estimates sampling error by resampling the original data.
bootstrapping, sampling error, re-sampling Statistical Bootstrapping
Model Kernel Density Estimation.ana
Demonstrates a very simple fixed-width kernel density estimator to estimate a "smooth" probability density.
kernel density estimation, kernel density smoothing Smooth PDF plots using Kernel Density Estimation
Model Output and input columns.ana
Presents an input table to a user, where one column is populated with computed output data, the other column with checkboxes for the user to select.
data analysis Output and Input Columns in Same Table
Model Platform2018b.ana offshore platforms, oil and gas, stakeholders, rigs to reefs, decision support
Determined how to decommission California's 27 offshore oil platforms.
decision analysis, multi-attribute, sensitivity analysis From Controversy to Consensus: California's offshore oil platforms
Model Comparing retirement account types.ana or Free 101 Compatible Version 401(k), IRA, retirement account, decision analysis, uncertainty
Explores tradeoffs between different retirement account types.
MultiTables, sensitivity analysis Retirement plan type comparison
Model Plane catching with UI 2020.ANA
Determines when you should leave home to catch an early morning plane departure.
decision theory, decision analysis, uncertainty, Monte Carlo simulation, value of information, EVPI, EVIU Plane Catching Decision with Expected Value of Including Uncertainty
Model Marginal Analysis for Control of SO2 Emissions.ana environmental engineering
Marginal analysis a.k.a. benefit/cost analysis to determine the policy alternative that leads us to the most economically efficient level of cleanup of acid rain from coal-burning electric-generating plants.
cost-benefit analysis, marginal analysis Marginal Analysis for Control of SO2 emissions
Model Donor-Presenter Dashboard.ana
Implements a continuous-time Markov chain in Analytica's discrete-time dynamic simulation environment. It supports immigration to, and emigration from, every node.
dynamic models, Markov processes Donor/Presenter Dashboard
Model Photosynthesis Regulation.ana - main regulation pathways
Photosystem.ana - rough sketch of genetic regulation
photosynthesis
A model of how photosynthesis is regulated inside a cyanobacteria. Simulates the concentration levels of key transport molecules along the chain.
dynamic models Regulation of Photosynthesis
Model Time-series-reindexing.ana
Examples of time-series re-indexing.
dynamic models, forecasting, time-series re-indexing Time-series re-indexing
Model Post Compression Model
Calculator for computing the maximum load that can be handled by a Douglas Fir - Larch post of a given size, grade, and composition in a construction setting.
Timber Post Compression Load Capacity
Model Compression Post Load Capacity.ana
Computes the load that a Douglas-Fir Larch post can support in compression. Works for different timber types and grades and post sizes.
compression analysis Compression Post Load Calculator
Model Daylighting analyzer.ana engineering
How to analyze lifecycle costs and savings from daylighting options in building design.
cost-benefits analysis Daylighting Options in Building Design
Model California Power Plants.ana power plants
Example showing how to use Choice menus and Checkbox inside an Edit table.
edit table, choice menu, pulldown menu, checkbox California Power Plants
Model Requires Analytica Optimizer
Electrical Transmission.ana
electrical engineering, power generation and transmission
Electrical network model that minimizes total cost of generation and transmission.
Electrical Generation and Transmission
Model Time of use pricing.ana & MECOLS0620.xlsx
(both files needed)
reading from spreadsheets, time-of-use pricing, electricity pricing
Time-of-use pricing is a rate tariff model used by utility companies that changes more during times when demand tends to exceed supply.
Time of Use pricing
Model Color map.ana
A model which highlights Cell Formatting and Computed Cell Formats.
computed cell formatting Color Map
Model World cup.ana
Demonstrates the Poisson distribution to determine why France beat Croatia in 2018.
2018 World Cup Soccer final
Model resnet18.zip residual network, deep residual learning, image recognition
Show it an image, and it tries to recognize what it is an image of, classifying it among 1000 possible categories. It uses an 18-layer residual network.
Image recognition
Model Month to quarter.ana
Shows how to transform an array from a finer-grain index (e.g., Month) onto a coarser index (e.g., Quarter). We generally refer to this as aggregation.
aggregation, level of detail, days, weeks, months, quarters, years Transforming Dimensions by transform matrix, month to quarter
Model Convolution.ana
Contains several examples of convolved functions.
signal analysis, systems analysis Convolution
Model Dependency Tracker.ana
This module tracks dependencies through your model, updating the visual appearance of nodes so that you can quickly visualize the paths by which one variable influences another.
dependency analysis Dependency Tracker Module
Model French-English.ana multi-lingual models
Maintains a single influence diagram with Title and Description attributes in both English and French.
Multi-lingual Influence Diagram
Model Parsing XML example.ana data extraction, xml, DOM parsing
Demonstrates two methods for extracting data: Using a full XML DOM parser, or using regular expressions.
Extracting Data from an XML file
Model Vector Math.ana
Functions used for computing geospatial coordinates and distances.
geospatial analysis, GIS, vector analysis Vector Math
Model Total Allowable w Optimizer.ana or
Total Allowable w StepInterp.ana for those without Optimizer
Determines how many fish or animals can be caught (landed) annually so that the probability of the population declining X% in Y years (decline threshold) is less than Z% (risk tolerance).
population analysis, dynamic models, optimization analysis Total Allowable Harvest
Model Cereal Formulation1.ana product formulation, cereal formulation
Cereal formulation/discrete mixed integer model that chooses product formulations to minimize total ingredient costs.
Linearizing a discrete NSP
Model Neural Network.ana feed-forward neural networks, non-linear regression
Models set up to train a 2-layer feedforward sigmoid network to "learn" the concept represented by the data set(s), and then test how well it does across examples not appearing in the training set.
optimization analysis Neural Network
Model Earthquake expenses.ana
An example of risk analysis with time-dependence and costs shifted over time.
risk analysis, cost analysis Earthquake Expenses
Best used with Analytica Optimizer
Loan policy selection.ana
creditworthiness, credit rating, default risk
Helps a lender decide optimal credit rating threshold to require and what interest rate (above prime) to charge.
risk analysis Loan Policy Selection
Model Hubbard_and_Seiersen_cyberrisk.ana cybersecurity risk
Simulates loss exceedance curves for a set of cybersecurity events, the likelihood and probabilistic monetary impact of which have been characterized by system experts.
loss exceedance curve, simulation Inherent and Residual Risk Simulation
Model media:Red State Blue State plot.ana map, states
Example containing the shape outlines for each of the 50 US states, along with a graph that uses color to depict something that varies by state.
graphing Red or blue state
Model COVID Model 2020--03-25.ana covid, covid-19, coronavirus, corona, epidemic
A systems dynamics style SICR model of the COVID-19 outbreak within the state of Colorado.
COVID-19 State Simulator, a Systems Dynamics approach
Model Corona Markov.ana covid, covid-19, coronavirus, corona, epidemic, sensitivity analyses
Explores the progression of the COVID-19 coronavirus epidemic in the US, and to explore the effects of different levels of social isolation.
How social isolation impacts COVID-19 spread in the US - A Markov model approach
Model Modelo Epidemiológoco para el Covid-19 con cuarentena.ana covid, covid-19, coronavirus, corona, epidemic
Un modelo en cadena de Markov del impacto previsto de la enfermedad coronavirus COVID-19 en el Perú, y del impacto del aislamiento social.
Epidemiological model of COVID-19 for Perú, en español
Model COVID-19 Triangle Suppression.ana covid, covid-19, coronavirus, corona, epidemic
Models progression of the COVID-19 pandemic in the US, understanding the amount of time that is required for lock down measures when a suppression strategy is adopted.
A Triangle Suppression model of COVID-19
Model Simple COVID-19.ana covid, covid-19, coronavirus, corona, epidemic
Explores possible COVID-19 Coronavirus scenarios from the beginning of March, 2020 through the end of 2020 in the US.
COVID-19 Coronavirus SICR progression for 2020
Model US COVID-19 Data.ana covid, covid-19, coronavirus, corona, epidemic, death, infection
Reads in COVID-19 data from the New York Times and transforms it into a form that is convenient to work with in Analytica.
COVID-19 Case and Death data for US states and counties
Model Voluntary vs mandatory testing.ana covid, covid-19, coronavirus, corona, epidemic
Computes the rate of infection in scenarios when COVID-19 testing is required/optional,based on prevalence rates, test accuracies and voluntary testing rates.
Mandatory vs Voluntary testing policies
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