CMU Artificial Intelligence Repository
BELIEF: Graphical Belief Function Models and Graphical
Probabilistic Models
areas/reasonng/probabl/belief/
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 almond@statsci.com for information about a
commercial version GRAPHICAL-BELIEF currently in the prototype stages.
Origin:
ftp.stat.washington.edu (128.95.17.34)
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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