Efficient inference in Bayes networks as a combinatorial optimization problem

Li, Zhaoyu; D'Ambrosio, Bruce

doi:10.1016/0888-613x(94)90019-1

Cited by 88 publications

(66 citation statements)

References 5 publications

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“…Our second practical application of local BNs concerns DC (Dechter 1996;Li and D'Ambrosio 1994;Zhang 1998). MSBNs are well established in the probabilistic Table 5 The computation needed in DC to process five localized queries in the original CHD BN in Fig.…”

Section: Lemma 6 Given As Input a Bn D The Output {Bmentioning

confidence: 99%

“…One approach, called multiply sectioned Bayesian networks (MSBNs) (Xiang 1996(Xiang , 2002Xiang and Jensen 1999;Xiang et al 2006Xiang et al , 2000Xiang et al , 1993, performs inference in sections of a BN. A second approach, called direct computation (DC) (Dechter 1996;Li and D'Ambrosio 1994;Zhang 1998), answers queries directly in the original BN. Although we focus on a third approach called join tree propagation, in which inference is conducted in a join tree (JT) (Pearl 1988;Shafer 1996) constructed from the DAG of a BN, our work has practical applications in all three approaches.…”

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confidence: 99%

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A join tree probability propagation architecture for semantic modeling

2008

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We propose the first join tree (JT) propagation architecture that labels the probability information passed between JT nodes in terms of conditional probability tables (CPTs) rather than potentials. By modeling the task of inference involving evidence, we can generate three work schedules that are more time-efficient for LAZY propagation. Our experimental results, involving five real-world or benchmark Bayesian networks (BNs), demonstrate a reasonable improvement over LAZY propagation. Our architecture also models inference not involving evidence. After the CPTs identified by our architecture have been physically constructed, we show that each JT node has a sound, local BN that preserves all conditional independencies of the original BN. Exploiting inference not involving evidence is used to develop an automated procedure for building multiply sectioned BNs. It also allows direct computation techniques to answer localized queries in local BNs, for which the empirical results on a real-world medical BN are promising. Screen shots of our implemented system demonstrate the improvements in semantic knowledge.

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Section: Lemma 6 Given As Input a Bn D The Output {Bmentioning

confidence: 99%

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confidence: 99%

A join tree probability propagation architecture for semantic modeling

2008

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“…Repeating the same procedure for all variables except X 5 would lead us to the desired result. This procedure is known as the variable elimination algorithm [16][17][18]. Thus, the idea that distinguishes this approach from the naïve approach outlined above is to organize the operations among the conditional distributions in the network, so that we do not manipulate distributions that are unnecessarily large.…”

Section: Inferencementioning

confidence: 99%

Inference in hybrid Bayesian networks

Langseth

Nielsen

Rumí

et al. 2009

Reliability Engineering & System Safety

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a b s t r a c tSince the 1980s, Bayesian networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability techniques (like fault trees and reliability block diagrams). However, limitations in the BNs' calculation engine have prevented BNs from becoming equally popular for domains containing mixtures of both discrete and continuous variables (the so-called hybrid domains). In this paper we focus on these difficulties, and summarize some of the last decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability.

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“…A variable is barren [13], if it is neither an evidence nor a target variable and it only has barren descendants. Probabilistic inference can be conducted directly in the original BN [6,9,12,13,17,18,19]. It can also be performed in a join tree [3,5,7,8,14].…”

Section: Probabilistic Inferencementioning

confidence: 99%

“…A second approach to BN inference is direct computation (DC), which performs inference directly in a BN. The classical DC algorithms are variable elimination (VE) [17,18,19], arc-reversal (AR) [9,13] and symbolic probabilistic inference (SPI) [6,12]. The experimental results provided by Zhang [17] indicate that VE is more efficient than the classical JTP methods when updating twenty or less non-evidence variables, given a set of twenty or fewer evidence variables.…”

Section: Introductionmentioning

confidence: 99%

An Improved LAZY-AR Approach to Bayesian Network Inference

Butz

Song

2006

Advances in Artificial Intelligence

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Abstract. We propose LAZY arc-reversal with variable elimination (LAZY-ARVE) as a new approach to probabilistic inference in Bayesian networks (BNs). LAZY-ARVE is an improvement upon LAZY arcreversal (LAZY-AR), which was very recently proposed and empirically shown to be the state-of-the-art method for exact inference in discrete BNs. The primary advantage of LAZY-ARVE over LAZY-AR is that the former only computes the actual distributions passed during inference, whereas the latter may perform unnecessary computation by constructing irrelevant intermediate distributions. A comparison between LAZY-AR and LAZY-ARVE, involving processing evidence in a real-world BN for coronary heart disease, is favourable towards LAZY-ARVE.

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Efficient inference in Bayes networks as a combinatorial optimization problem

Cited by 88 publications

References 5 publications

A join tree probability propagation architecture for semantic modeling

A join tree probability propagation architecture for semantic modeling

Inference in hybrid Bayesian networks

An Improved LAZY-AR Approach to Bayesian Network Inference

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