Multiply sectioned Bayesian networks for neuromuscular diagnosis

Xiang, Yang; Pant, Bhaskar; Eisen, Andrew; Beddoes, Michael P.; Poole, David

doi:10.1016/0933-3657(93)90019-y

Cited by 37 publications

(24 citation statements)

References 12 publications

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“…That is, practical experience has previously demonstrated that queries tend to involve variables in close proximity within the BN (Xiang et al 1993). We conclude our discussion by showing how our semantic architecture allows DC to exploit localized queries.…”

Section: Localizedmentioning

confidence: 58%

“…We also have suggested a method for exploiting localized queries in DC techniques. Practical experience, such as that gained from neuromuscular diagnosis (Xiang et al 1993), has demonstrated that queries tend to involve variables in close proximity within a BN. Our approach allows DC to process localized queries in local BNs.…”

Section: Resultsmentioning

confidence: 99%

“…Our first practical application of local BNs concerns MSBNs (Xiang 1996(Xiang , 2002Xiang and Jensen 1999;Xiang et al 2006Xiang et al , 2000Xiang et al , 1993, which are formally defined as follows. A MSBN is a finite set {B 1 , B 2 , .…”

Section: Local Bns and Practical Applicationsmentioning

confidence: 99%

“…While it is acknowledged that our suggestion here is beneficial only for localized queries, practical experience with BNs, such as in neuromuscular diagnosis (Xiang et al 1993), has long established that localized queries are a reality.…”

Section: Example 28mentioning

confidence: 99%

“…Although Cooper (1990) has shown that the complexity of exact inference in BNs is NP-hard, several approaches have been developed that apparently work quite well in practice. 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.…”

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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: Localizedmentioning

confidence: 58%

Section: Resultsmentioning

confidence: 99%

Section: Local Bns and Practical Applicationsmentioning

confidence: 99%

Section: Example 28mentioning

confidence: 99%

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confidence: 99%

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A join tree probability propagation architecture for semantic modeling

2008

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Verification of DAG structures in cooperative belief network-based multiagent systems

Xiang

1998

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Multiply sectioned Bayesian networks (MSBNs) provide a framework for probabilistic reasoning in a complex single user oriented system as well as in a cooperative multi-agent distributed interpretation system. During the construction or dynamic formation of a MSBN, automatic verification of the acyclicity of the overall structure is desired. Well known algorithms for acyclicity test assume a centralized storage of the structure to be tested. We discuss why a centralized test is undesirable and propose a distributed algorithm that verifies the acyclicity through cooperation among subnets/agents. The algorithm does not require each agent to reveal its internal structure and thus supports construction of a MSBN from subnets built by different vendors.

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Cooperative triangulation in MSBNs without revealing subnet structures

Xiang

2000

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Multiply sectioned Bayesian networks (MSBNs) provide a coherent framework for probabilistic inference in a cooperative multiagent distributed interpretation system. Inference in MSBNs can be performed effectively using a compiled representation. The compilation involves the triangulation of the collective dependency structure (a graph) defined in terms of the union of a set of local dependency structures (a set of graphs). Privacy of agents eliminates the option to assemble these graphs at a central location and to triangulate their union. Earlier work solved distributed triangulation in a restricted case. The method is conceptually complex and the correctness of its extension to the general case is difficult to justify. In this paper, we present a new method that is conceptually simpler and is efficient. We prove its correctness in the general case and demonstrate its performance experimentally. © 2001 John Wiley & Sons, Inc.

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Multiply sectioned Bayesian networks for neuromuscular diagnosis

Cited by 37 publications

References 12 publications

A join tree probability propagation architecture for semantic modeling

A join tree probability propagation architecture for semantic modeling

Verification of DAG structures in cooperative belief network-based multiagent systems

Cooperative triangulation in MSBNs without revealing subnet structures

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