Incorporating Evidence in Bayesian Networks with the Select Operator

Butz, Cory J.; Fang, F.

doi:10.1007/11424918_31

Cited by 4 publications

(4 citation statements)

References 3 publications

(9 reference statements)

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“…The same confusion occurs in Bayesian network software, where at least four products use soft evidence to refer to likelihood evidence (see Table 1). In another paper [12], the term was used to refer to a variable X which does not take a specific value x, that is to say X = x, which is usually called a negative finding. Moreover, the concept of uncertain evidence specified by a local probability distribution and that cannot be modified by other information is not always named soft evidence ( Table 2).…”

Section: Soft Evidence : a Terminology With No Consensusmentioning

confidence: 99%

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%

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

et al. 2015

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“…However, the use of the term soft evidence is abandoned in [12]. In another paper [9], soft evidence means that a variable X does not take a specific value x, that is to say X = x, which is usually called a negative finding. Most available software for non-deterministic evidence propagation in BN engines implement Pearl's method of virtual evidence.…”

Section: About the Use Of The Terms Soft Evidencementioning

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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“…Traditionally, when an agent collects hard and soft evidence E [2], two tasks are carried out: (i) the agent updates its knowledge base with E, and (ii) all other agents are then updated with E. On the contrary, if the MABN satisfies the inclusion principle, then step (ii) is not necessarily required. Table 5, satisfies the inclusion principle.…”

Section: Examplementioning

confidence: 99%

“…Suppose Agent 2 collects the soft evidence b ≠ 2 and c ≠ 2. It performs task (i) by updating its knowledge base p 2 (d | b, c) using the selection operator σ [2], as illustrated in Table 4. However, performing task (ii) is unnecessary, since the updated MABN in Table 5 still satisfies the inclusion principle.…”

Section: Examplementioning

confidence: 99%