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BELIEF: Graphical Belief Function Models and Graphical Probabilistic Models

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). Version 1.2 corresponds to CLtL2 and Version 1.1 to CLtL1. Contact the author at for information about a commercial version GRAPHICAL-BELIEF currently in the prototype stages.
Origin: (

Version: 1.2 (6-MAR-92) Requires: Common Lisp Ports: Tested in Allegro CL 4.1 Copying: Copyright (c) 1989, 1990, 1992 Russell G. Almond Use, copying, and distribution permitted for education or research purposes. CD-ROM: Prime Time Freeware for AI, Issue 1-1 Author(s): Russell Almond or StatSci (a division of MathSoft, Inc.) 1700 Westlake Ave., N Suite 500 Seattle, WA 98109 Tel: (206) 283-8802 Keywords: Authors!Almond, Bayesian Networks, Bayesian Probability, Belief, Dempster-Shafer, Graphical Models, Influence Diagrams, Lisp!Code, Probabilistic Reasoning, Reasoning!Probabilistic Reasoning, Relevance Diagrams, Second Order Models References: ?
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