This code is a basic implementation of Judea Pearl's belief propagation algorithm for tree-structured Bayesian belief networks.
Tree-shaped Bayesian Networks only
BELIEF is a Common Lisp implementation of the Dempster and Kong fusion and propagation algorithm for Graphical Belief Function Models and the Lauritzen and Spiegelhalter algorithm for Graphical Probabilistic Models. It includes code for manipulating graphical belief models such as Bayes Nets and Relevance Diagrams (a subset of Influence Diagrams) using both belief functions and probabilities as basic representations of uncertainty. It uses the Shenoy and Shafer version of the algorithm, so one of its unique features is that it supports both probability distributions and belief functions. It also has limited support for second order models (probability distributions on parameters).
1.2 (6-MAR-92)
Allegro CL 4.2
Probabilities/Belief Functions, Parameter Uncertainty
GUI, Utility
Probabilities, API programming interface, development and runtime GUIs Utilities
Belief Functions, Parameter Uncertainty.
Windows 3.1, NT, 95, C++
License Required, Free to academic users for non-commerical, research purposes
1.2 - 12/16/93
Common Lisp (CLIM based GUI available)
Research purposes' only
Probabilities, Utilities, Influence Diagrams, GUI (IDEAL-EDIT)
Parameter Uncertainty, Belief Functions
Graphical-Belief is tool for exploring the predictive aspects of models. It is based on the technology of graphical models (also known as influence diagrams, belief networks or Bayes nets) which has already become a standard in decision analysis, statistics and artificial intelligence. These models have been used successfully in such diverse areas as system level reliability, medical decision making, financial planning and operations managment.
2.0 beta
Allegro CL 4.2
GUI, Probabilities/Belief Functions, Parameter Uncertainty, Knowledge Based Model Construction
1989
Macintosh
No resale
Probabilities/Belief Functions, GUI
Utilities
Windows 95, Windows NT (supports API for Visual Basic 4.0, C or C++)
Non-Commerical uses only and Licence Required
Probabilities, Utilities, Influence Diagrams, Standard Rulebase
Belief Functions, Restricted to single decision influence diagram
Common Lisp (CLtL1) (GUI in Allegro CL)
Non-commercial use only
Probabilities/Belief Functions, Possibilities, GUI
Utilities
MIT Scheme
Probabilities
Belief Functions, Utilities, GUI
Common Lisp (Tcl/Tk Front end).
Research only, no resale.
Probabilities, Local Expression Language Utilities, Explanation, Dynamic Models, GUI.
Belief Functions, Documentation.
Analytica is a Macintosh-based, visual environment for creating, analyzing and communicating probabilistic models for business, risk and decision analysis. It is the successor to Lumina's Demos decision modeling system for the Macintosh.
Macintosh (Windows version will be available shortly supporting libraries for Visual Basic, Delphi, and C++)
Professional v1.0 (October 1996)
Probabilities/Belief Functions, Hierarchical Influence Diagrams, Parameter Uncertainty, links to spreadsheets and simulations
Application on Macintosh, Windows (summer 96), Unix (next year) API available on all platforms.
License Required
Probabilities, Utilities, Influence Diagrams, API.
Belief Functions, Parameter Uncertainty.
More advanced learning, integration with Case Based Reasoning, real-time control
Netica:
Netica API: