Bayesian Network Revision With Probabilistic Constraints

Peng, Yun; Ding, Zhongli; Zhang, Shenyong; Pan, Rong

doi:10.1142/s021848851250016x

Cited by 4 publications

(6 citation statements)

References 12 publications

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“…The term soft evidence is used with that meaning by Valtorta [40,48,49,69] and other authors [43,60,63,64,68]. With that meaning, likelihood evidence can be interpreted as evidence with uncertainty, while soft evidence can be interpreted as evidence of uncertainty [64].…”

Section: Soft Evidence : a Terminology With No Consensusmentioning

confidence: 99%

“…Revision of the model occurs when hypotheses associated with it are changed. Several methods for knowledge integration and Bayesian network revision have been proposed [63].…”

Section: Probabilistic Evidence Propagation Versus Model Revisionmentioning

confidence: 99%

“…These adaptations are the E-IPFP, E-IPFP-SMOOTH and D-IPFP algorithms [63]. The E-IPFP algorithm integrates the constraints by only changing the conditional probability tables of the given Bayesian network while preserving the network structure.…”

Section: Probabilistic Evidence Propagation Versus Model Revisionmentioning

confidence: 99%

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An explication of uncertain evidence in Bayesian networks: likelihood evidence and probabilistic evidence

et al. 2015

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This paper proposes a systematized presentation and a terminology for observations in a Bayesian network. It focuses on the three main concepts of uncertain evidence, namely likelihood evidence and fixed and not-fixed probabilistic evidence, using a review of previous literature. A probabilistic finding on a variable is specified by a local probability distribution and replaces any former belief in that variable. It is said to be fixed or not fixed regarding whether it has to be kept unchanged or not after the arrival of observation on other variables. Fixed probabilistic evidence Véronique Delcroix Science and Enterprise Parks, LE11 3TU Leicestershire, UK is defined by Valtorta et al. (J Approx Reason 29(1):71-106 2002) under the name soft evidence, whereas the concept of not-fixed probabilistic evidence has been discussed by Chan and Darwiche (Artif Intell 163(1):67-90 2005). Both concepts have to be clearly distinguished from likelihood evidence defined by Pearl (1988), also called virtual evidence, for which evidence is specified as a likelihood ratio, that often represents the unreliability of the evidence. Since these three concepts of uncertain evidence are not widely understood, and the terms used to describe these concepts are not well established, most Bayesian networks engines do not offer well defined propagation functions to handle them. Firstly, we present a review of uncertain evidence and the proposed terminology, definitions and concepts related to the use of uncertain evidence in Bayesian networks. Then we describe updating algorithms for the propagation of uncertain evidence. Finally, we propose several results where the use of fixed or not-fixed probabilistic evidence is required.

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Section: Soft Evidence : a Terminology With No Consensusmentioning

confidence: 99%

“…Revision of the model occurs when hypotheses associated with it are changed. Several methods for knowledge integration and Bayesian network revision have been proposed [63].…”

Section: Probabilistic Evidence Propagation Versus Model Revisionmentioning

confidence: 99%

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An explication of uncertain evidence in Bayesian networks: likelihood evidence and probabilistic evidence

et al. 2015

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“…This section concerns the propagation of several fixed probabilistic findings. These algorithms solutions are suitable for Bayesian networks.This synthesis is inspired by [44,45]. Most of them are based on the Iterative Proportional Fitting Procedure (IPFP) algorithm [17], [25], [32] which is an iterative method of revising a probability distribution to respect a set of given probability constraints in the form of posterior marginal probability distributions over a subset of variables.…”

Section: Propagating Fixed Probabilistic Evidencementioning

confidence: 99%

“…The term "probabilistic evidence" is also inspired from two contributions: (1) in the context of Bayesian network revision, the input is named a probabilistic constraint [45]. Likelihood evidence is represented as a likelihood ratio whereas probabilistic evidence is specified by a probability distribution of one or more variables.…”

Section: Introductionmentioning

confidence: 99%

From Information to Evidence in a Bayesian Network

Mrad

Delcroix

Piechowiak

et al. 2014

Probabilistic Graphical Models

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Evidence in a Bayesian network comes from information based on the observation of one or more variables. A review of the terminology leads to the assessment that two main types of non-deterministic evidence have been defined, namely likelihood evidence and probabilistic evidence but the distinction between fixed probabilistic evidence and not fixed probabilistic evidence is not clear, and neither terminology nor concepts have been clearly defined. In particular, the term soft evidence is confusing. The article presents definitions and concepts related to the use of non-deterministic evidence in Bayesian networks, in terms of specification and propagation. Several examples help to understand how an initial piece of information can be specified as a finding in a Bayesian network.

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