Algorithms for Bayesian Belief-Network Precomputation

From Tetherless World Wiki

Jump to: navigation, search

Citation: Edward Herskovits and Gregory F. Cooper. (1991) Algorithms for Bayesian Belief-Network Precomputation. In KSL-89-35, 1991.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Edward Herskovits and Gregory F. Cooper
title Algorithms for Bayesian Belief-Network Precomputation
number KSL-89-35
institution Knowledge Systems, AI Laboratory
year 1991
Bibtex more
Access Paper
abstract Bayesian belief networks show promise as a representational framework for constructing expert systems; they provide platforms for knowledge acquisition and for normative probabilistic inference. Despite the intuitive appeal of this inference paradigm, the run-time complexity of general belief-network computation may be too great for solving many complex problems in a practical amount of time. Therefore, researchers have focused their attention on developing approximate or special-case algorithms for belief-network inference. For belief networks with a highly skewed distribution of joint probabilities, storing a small number of cases to capture a large proportion of the likely uses of the network may lead to a significant increase in the speed of inference. We report here preliminary results of a set of algorithms that cache (precompute and store) a small subset of a belief network to decrease the expected running time for probability computation.

KSL Technical Report ID: KSL-89-35
Facts about Algorithms for Bayesian Belief-Network PrecomputationRDF feed
Abstract Bayesian belief networks show promise as a … Bayesian belief networks show promise as a representational framework for constructing expert systems; they provide platforms for knowledge acquisition and for normative probabilistic inference. Despite the intuitive appeal of this inference paradigm, the run-time complexity of general belief-network computation may be too great for solving many complex problems in a practical amount of time. Therefore, researchers have focused their attention on developing approximate or special-case algorithms for belief-network inference. For belief networks with a highly skewed distribution of joint probabilities, storing a small number of cases to capture a large proportion of the likely uses of the network may lead to a significant increase in the speed of inference. We report here preliminary results of a set of algorithms that cache (precompute and store) a small subset of a belief network to decrease the expected running time for probability computation. running time for probability computation.
Author Edward Herskovits and Gregory F. Cooper  +
Bibtype techreport  +
Has author Edward Herskovits and Gregory F. Cooper  +
Has identifier KSL-89-35  +
Has publishing details 1991  +
Has title Algorithms for Bayesian Belief-Network Precomputation  +
Has where published KSL-89-35  +
Has year 1991  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-89-35  +
Number KSL-89-35  +
Process note YES  +
Title Algorithms for Bayesian Belief-Network Precomputation  +
Year 1991  +
Retrieved from "http://tw.rpi.edu/wiki/Algorithms_for_Bayesian_Belief-Network_Precomputation"
Personal tools