Modelling Guide

This section give tips and guidelines from the experts on how to model with Analytica.

• Managing Memory and CPU Time for large models

• Array Manipulation Examples and Challenge Problems

• How to translate Excel spreadsheets into Analytica

• Debugging Hints

• Articles that refer to Analytica

• Writing Array-Abstractable Definitions

Concepts

• Array Abstraction
• Evaluation Modes
• Modules and Libraries : Organizing models, storing in separate files for collaboration or reuse.
• Expressing Uncertainty : Distributions, sampling methods, the Run index, etc.
• Intelligent Multi-Dimensional Arrays
• Associative vs. Positional Indexing
• Self-Indexed Arrays
• Implicit Dimensions (aka Lists, null-indexed arrays, unnamed dimensions)
• User-Defined Functions
• Using References
• Modeling Changes over Time
• Parametric Analysis
• Optimization
  • Linear and Quadratic Programming
  • Non-linear Programming
• Scripting : Button scripts, typescript
• Special Values INF, NAN, Null, and Undefined
• Local Indexes
• Table Splicing - edit tables based on computed indexes
• OLE Linking
• Model building by mouse Enabling modelers to build models by copying nodes and drawing arrows
• Analytica Object Classes
• Controlling When Result Are Cached
• Encoding Decision Trees as influence diagrams
• Proactive Evaluation

Mini-Tutorials

Currently, the ones here are focused on features new to 4.0. But more will be coming. For more material along these lines, see the list of past Analytica User Group webinars at Analytica User Group.

• Graphing
  • Introduction to the Coordinate Index
    • Graphing a Probability Density
    • Making a Multi-D Scatter Plot
  • Tornado Plots
  • Gantt Charts
  • Waterfall Charts
  • Contour/Region Charts

• Edit Tables
  • Inserting Choice Controls in Edit Table Cells
  • Introduction to SubTables

• Integration with External Programs and Data.
  • Retrieving Content From the Web
  • Querying an ODBC data source

Suggested Topics

Please list other areas for which you would like tips or guidelines, or add your own:

• Choosing the Sample Size

• Dynamic simulation

• Programs that do things with and to models

• How to make models transparent and easy to understand