Expert Systems and Probabilistic Network Models

Castillo, Enrique; Gutiérrez, José Manuel; Hadi, Ali S.

doi:10.1007/978-1-4612-2270-5

Cited by 506 publications

(386 citation statements)

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“…Considerations of efficiency make it often advisable to transform a graphical model into a form that is better suited for propagating the evidential knowledge and computing the resulting marginal distributions for the unobserved attributes. We briefly sketch here a popular efficient reasoning method known as clique tree propagation (CTP) [33,8], which involves transforming the conditional independence graph into a clique tree.…”

Section: Evidence Propagationmentioning

confidence: 99%

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Possibilistic Graphical Models

Borgelt

Gebhardt

Kruse

2000

Computational Intelligence in Data Mining

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Graphical modeling is an important method to efficiently represent and analyze uncertain information in knowledge-based systems. Its most prominent representatives are Bayesian networks and Markov networks for probabilistic reasoning, which have been well-known for over ten years now. However, they suffer from certain deficiencies, if imprecise information has to be taken into account. Therefore possibilistic graphical modeling has recently emerged as a promising new area of research. Possibilistic networks are a noteworthy alternative to probabilistic networks whenever it is necessary to model both uncertainty and imprecision. Imprecision, understood as set-valued data, has often to be considered in situations in which information is obtained from human observers or imprecise measuring instruments. In this paper we provide an overview on the state of the art of possibilistic networks w.r.t. to propagation and learning algorithms.

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Section: Evidence Propagationmentioning

confidence: 99%

“…Applications of graphical models can be found in a large variety of areas including diagnostics, expert systems, planning, data analysis, and control. For an overview, see [8].…”

Section: Introductionmentioning

confidence: 99%

Possibilistic Graphical Models

Borgelt

Gebhardt

Kruse

2000

Computational Intelligence in Data Mining

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“…The family F i of a variable v i in a DAG is {v i } ∪ P i . A numbering ≺ of the variables in a DAG is called ancestral [1], if the number corresponding to any variable v i is lower than the number corresponding to each of its children…”

Section: Fig 1 the Coronary Heart Disease (Chd) Bn [2] In Examplementioning

confidence: 99%

“…Bayesian networks (BNs) [1,2,10,14] are an established framework for uncertainty management in artificial intelligence. A BN consists of a directed acyclic graph (DAG) and a corresponding set of conditional probability tables (CPTs).…”

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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“…At the core of the theory of graphical models [10,6,9,2,1], that is, of Bayes networks and Markov networks, is the notion of a so-called conditional independence graph or independence map for a given multidimensional probability distribution. It allows us to determine the conditional independence statements obtaining in the probability distribution by applying a simple graph theoretic criterion, which is based on node separation.…”

Section: Introductionmentioning

confidence: 99%

On Identifying Tree-Structured Perfect Maps

Borgelt

2003

KI 2003: Advances in Artificial Intelligence

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Abstract. It is well known that tree-structured perfect maps can be uniquely identified by computing a maximum weight spanning tree with mutual information providing the edge weights. In this paper I generalize the edge evaluation measure by stating the conditions such a measure has to satisfy in order to be able to identify tree-structured perfect maps. In addition, I show that not only mutual information, but also the wellknown χ 2 measure satisfies these conditions.

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Expert Systems and Probabilistic Network Models

Abstract: Library of Congress Catologing-in-Publication Data Castillo. Enrique. Expert systems and probabilistic network models I Enrique Castillo, Jose Manuel Gutierrez, Ali S. Hadi. p. cm.-(Monographs in computer science) Includes bibliographical references and index.

Cited by 506 publications

References 0 publications

Possibilistic Graphical Models

Possibilistic Graphical Models

An Improved LAZY-AR Approach to Bayesian Network Inference

On Identifying Tree-Structured Perfect Maps

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