Bayesian Belief-Network Inference Using Recursive Decomposition

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Citation: Gregory F. Cooper. (1992) Bayesian Belief-Network Inference Using Recursive Decomposition. In KSL-90-05, July,1992.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Gregory F. Cooper
title Bayesian Belief-Network Inference Using Recursive Decomposition
number KSL-90-05
institution Knowledge Systems, AI Laboratory
address Stanford, CA, USA
year 1992
month July
Bibtex more
Access Paper
abstract A Bayesian belief network uses a directed acyclic graph to represent probabilistic dependencies among a set of variables. Typically, performing inference on a belief network involves computing conditional probabilities among variables in the network. We introduce an algorithm for belief-network inference that is based on recursive decomposition. The algorithm recursively bisects a belief network to create a binary tree. The tree then is used for probabilistic inference. We describe the recursive-decomposition inference algorithm in sufficient detail for it to be implemented readily, and we prove the validity of the algorithm. We also link belief-network inference that is based on recursive decomposition to the literature on vertex separators. The recursive divide-and-conquer nature of the recursive-decomposition inference algorithm allows an implementation that is brief and simple; it also facilitates our analysis of the time complexity of the algorithm. A companion paper contains an analysis and evaluation of the inference algorithm on numerous belief network structures.

KSL Technical Report ID: KSL-90-05
Facts about Bayesian Belief-Network Inference Using Recursive DecompositionRDF feed
Abstract A Bayesian belief network uses a directed … A Bayesian belief network uses a directed acyclic graph to represent probabilistic dependencies among a set of variables. Typically, performing inference on a belief network involves computing conditional probabilities among variables in the network. We introduce an algorithm for belief-network inference that is based on recursive decomposition. The algorithm recursively bisects a belief network to create a binary tree. The tree then is used for probabilistic inference. We describe the recursive-decomposition inference algorithm in sufficient detail for it to be implemented readily, and we prove the validity of the algorithm. We also link belief-network inference that is based on recursive decomposition to the literature on vertex separators. The recursive divide-and-conquer nature of the recursive-decomposition inference algorithm allows an implementation that is brief and simple; it also facilitates our analysis of the time complexity of the algorithm. A companion paper contains an analysis and evaluation of the inference algorithm on numerous belief network structures. thm on numerous belief network structures.
Address Stanford, CA, USA  +
Author Gregory F. Cooper  +
Bibtype techreport  +
Has author Gregory F. Cooper  +
Has identifier KSL-90-05  +
Has publishing details July,1992  +
Has title Bayesian Belief-Network Inference Using Recursive Decomposition  +
Has where published KSL-90-05  +
Has year 1992  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-90-05  +
Month July  +
Number KSL-90-05  +
Process note NO  +
Title Bayesian Belief-Network Inference Using Recursive Decomposition  +
Year 1992  +
Retrieved from "http://tw.rpi.edu/wiki/Bayesian_Belief-Network_Inference_Using_Recursive_Decomposition"
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