Initialization for the method of conditioning in Bayesian belief networks

Suermondt, Henri J.; Cooper, Gregory F.

doi:10.1016/0004-3702(91)90091-w

Cited by 22 publications

(9 citation statements)

References 3 publications

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“…only one path should exist between any two nodes. Otherwise the network is considered to have loops and various methods exist in the literature for handling them (Suermondt andCooper 1991, Pearl 1988). …”

Section: Bayesian Networkmentioning

confidence: 99%

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Application of a Bayesian network in a GIS based decision making system

Stassopoulou¹,

Πέτρου²,

Kittler³

1998

International Journal of Geographical Information Science

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In this paper we show how a Pearl Bayes network of inference can be used with a GIS in order to combine information from different sources of data for the purpose of classification. Data may include satellite images, topographic maps, geological maps etc, each one with its own resolution and accuracy. We show how this uncertainty in the input data is incorporated in the network and develop also a method to construct the conditional probability matrices used by the network. We demonstrate our approach within the framework of the problem of assessing the risk of desertification of some burned forests in the Mediterranean region.

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Section: Bayesian Networkmentioning

confidence: 99%

“…In our network there are two paths between S Dand D and in order to handle the loop we used the method of conditioning(reasoning by assumptions) which is based on the ability to change the connectivity of a network and render it singly connected by instantiating a selected group of variables (Suermondt andCooper 1991, Pearl 1988). …”

Section: Problem Formulationmentioning

confidence: 99%

Application of a Bayesian network in a GIS based decision making system

Stassopoulou¹,

Πέτρου²,

Kittler³

1998

International Journal of Geographical Information Science

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“…In the process, we worked out many of . the technical details that previously were unspecified [22]. In particular, we examined cutset conditioning on multiply-connected networks.…”

Section: Studying and Extending Cutset Conditioningmentioning

confidence: 99%

Computational Techniques for Probabilistic Inference

Shortliffe¹,

Cooper²

1991

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“…Exact algorithms have explored Expression (1) to order operations efficiently [24,45], sometimes using auxiliary junction trees [15,42]. A few algorithms exploit conditioning operations [51,57] that reduce inference to manipulation of polytrees. These conditioning algorithms employ loop cutsets: a loop cutset is a set of edges that, once removed, leaves the underlying undirected graph as a tree [51].…”

Section: Introductionmentioning

confidence: 99%

Approximate algorithms for credal networks with binary variables

Ide¹,

Cozman²

2008

International Journal of Approximate Reasoning

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This paper presents a family of algorithms for approximate inference in credal networks (that is, models based on directed acyclic graphs and set-valued probabilities) that contain only binary variables. Such networks can represent incomplete or vague beliefs, lack of data, and disagreements among experts; they can also encode models based on belief functions and possibilistic measures. All algorithms for approximate inference in this paper rely on exact inferences in credal networks based on polytrees with binary variables, as these inferences have polynomial complexity. We are inspired by approximate algorithms for Bayesian networks; thus the Loopy 2U algorithm resembles Loopy Belief Propagation, while the IPE and SV2U algorithms are respectively based on Localized Partial Evaluation and variational techniques.

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Initialization for the method of conditioning in Bayesian belief networks

Cited by 22 publications

References 3 publications

Application of a Bayesian network in a GIS based decision making system

Application of a Bayesian network in a GIS based decision making system

Computational Techniques for Probabilistic Inference

Approximate algorithms for credal networks with binary variables

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