A Randomized Approximation Algorithm for Probabilistic Inference on Bayesian Belief Networks

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Citation: R. Martin Chavez and Gregory F. Cooper. (1989) A Randomized Approximation Algorithm for Probabilistic Inference on Bayesian Belief Networks. In KSL-88-72, October,1989.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author R. Martin Chavez and Gregory F. Cooper
title A Randomized Approximation Algorithm for Probabilistic Inference on Bayesian Belief Networks
number KSL-88-72
institution Knowledge Systems, AI Laboratory
year 1989
month October
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abstract Researchers in decision analysis and artificial intelligence (AI) have used Bayesian belief networks to build probabilistic expert systems. Using standard methods drawn from the theory of computational complexity, workers in the field have shown that the problem of probabilistic inference in belief networks is difficult and almost certainly intractable. We have developed a randomized approximation scheme, BN-RAS, for doing probabilistic inference in belief networks. The algorithm can, in many circumstances, perform efficient approximate inference in large and richly interconnected models. Unlike previously described stochastic algorithms for probabilistic inference, the randomized approximation scheme (ras) computes a priori bounds on running time by analyzing the structure and contents of the belief network. In this article, we describe BN-RAS precisely and analyze its performance mathematically.

KSL Technical Report ID: KSL-88-72
Facts about A Randomized Approximation Algorithm for Probabilistic Inference on Bayesian Belief NetworksRDF feed
Abstract Researchers in decision analysis and artif … Researchers in decision analysis and artificial intelligence (AI) have used Bayesian belief networks to build probabilistic expert systems. Using standard methods drawn from the theory of computational complexity, workers in the field have shown that the problem of probabilistic inference in belief networks is difficult and almost certainly intractable. We have developed a randomized approximation scheme, BN-RAS, for doing probabilistic inference in belief networks. The algorithm can, in many circumstances, perform efficient approximate inference in large and richly interconnected models. Unlike previously described stochastic algorithms for probabilistic inference, the randomized approximation scheme (ras) computes a priori bounds on running time by analyzing the structure and contents of the belief network. In this article, we describe BN-RAS precisely and analyze its performance mathematically. nd analyze its performance mathematically.
Author R. Martin Chavez and Gregory F. Cooper  +
Bibtype techreport  +
Has author R. Martin Chavez and Gregory F. Cooper  +
Has identifier KSL-88-72  +
Has publishing details October,1989  +
Has title A Randomized Approximation Algorithm for Probabilistic Inference on Bayesian Belief Networks  +
Has where published KSL-88-72  +
Has year 1989  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-88-72  +
Month October  +
Number KSL-88-72  +
Process note YES  +
Title A Randomized Approximation Algorithm for Probabilistic Inference on Bayesian Belief Networks  +
Year 1989  +
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