Fuzzy Bayesian Networks — A General Formalism for Representation, Inference and Learning With Hybrid Bayesian Networks

Pan, Heping; Liu, Lin

doi:10.1142/9789812797599_0008

Cited by 5 publications

(7 citation statements)

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“…More information about the differences between soft evidence and virtual evidence can be found in (Valtorta, Kim, & Vomlel, 2002). • The methods presented in references (Pan, 2000;Yang, 1997) achieve the same ultimate results as SBNs: Smoothing the posterior probabilities of the hypotheses given the value of continuous variables. However, the procedures (fuzzy logic, gaussian functions, etc.)…”

Section: Related Workmentioning

confidence: 95%

“…In (Pan, 2000;Pan, Okello, McMichael, & Roughan, 1998) two different components in uncertainty are distinguished: Uncertainty in the output of a clearly defined and randomly occurring event (described by a probability) and uncertainty inherent in the description of the event itself (described by fuzzy logic). Thus, in those papers, an integration of BNs and fuzzy logic, called fuzzy causal probabilistic networks (FCPN) is proposed.…”

Section: Related Workmentioning

confidence: 99%

“…A fuzzifier converts a continuous variable to a fuzzy variable, whereas a defuzzifier does the inverse. In (Pan, 2000) conditional gaussian models are used for mapping between continuous variables and their partner fuzzy variables. In (Yang, 1997) a methodology is presented to estimate the pdf of a continuous variable from the probabilities of the discrete variables in the BN.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Automatic diagnosis of mobile communication networks under imprecise parameters

Barco

Díez

Wille

et al. 2009

Expert Systems with Applications

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Section: Related Workmentioning

confidence: 95%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Automatic diagnosis of mobile communication networks under imprecise parameters

Barco

Díez

Wille

et al. 2009

Expert Systems with Applications

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“…Despite a number of papers dealing with fuzzy Bayesian networks, for example [3,28,59,67], there is still no commonly accepted definition of this concept. However, one of the objectives of these is to manage uncertainties in the input of the Bayesian Network.…”

Section: Probabilistic Evidence and An Example Of Fuzzy/bayesian Networkmentioning

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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“…Several other methods have been developed for causal diagnosis based on observed fuzzy symptoms by integrating fuzzy logic with other approaches such as neural networks [25,26,28], belief networks based on Bayesian theory [19,18,17,13,2] and Dempster-Shafer theory or Evidence theory [3,6] and possibility theory [5,4]. There are certain difficulties associated with these methods, such as the large volume of data required to train neural networks; knowledge of prior probability and conditional probability distributions required for belief networks based on Bayesian theory; subjective judgment on degrees of belief and computational complexities in evidence propagation in networks for belief networks based on DempsterShafer theory, and large volume of data required to train possibilistic networks.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic based assignable cause diagnosis using control chart patterns

Demirli

Vijayakumar

2010

Information Sciences

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a b s t r a c tControl chart patterns, besides determining the presence of assignable causes, also provide hints on the nature of assignable cause(s) present. Relating the patterns exhibited on the control chart to assignable causes is an ambiguous and vague task especially when multiple patterns co-exist. In this work, a rule based fuzzy inference system is developed for X control chart to prioritize the control chart causes based on the accumulated evidence. When a process goes out of control, search for assignable causes can be assisted by the priorities assigned to the causes. For an in-control process, developing patterns can be tracked and preventive action can be taken to prevent the process from going out of control.

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Fuzzy Bayesian Networks — A General Formalism for Representation, Inference and Learning With Hybrid Bayesian Networks

Cited by 5 publications

References 0 publications

Automatic diagnosis of mobile communication networks under imprecise parameters

Automatic diagnosis of mobile communication networks under imprecise parameters

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

Fuzzy logic based assignable cause diagnosis using control chart patterns

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