Parameter adjustment in Bayes networks. The generalized noisy OR–gate

Díez, F. J.

doi:10.1016/b978-1-4832-1451-1.50016-0

Cited by 147 publications

(109 citation statements)

References 10 publications

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“…..} be a partition of a set X of uncertain causes of effect e and sets in R satisfy Eqns (1) and (2). Then, interaction among sets of causes in R is reinforcing.…”

Section: Lemma 1 Letmentioning

confidence: 99%

“…Noisy-MAX model [1] becomes noisy-OR model when variables are binary. Therefore, from Lemma 1, when domain is binary, noisy-MAX represents only reinforcing interaction.…”

Section: Related Models Of Causal Interactionmentioning

confidence: 99%

“…Henrion [5] added to noisy-OR model the leaky probability. Diez [1] and Srinivas [9] extended noisy-OR from binary to multi-valued variables. Heckerman and Breese [4] analyzed a collection of causal independence relations that allows efficient acquisition of conditional probability tables in BNs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling Causal Reinforcement and Undermining with Noisy-AND Trees

Xiang

Jia

2006

Advances in Artificial Intelligence

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Abstract. Causal modeling, such as noisy-OR, reduces probability parameters to be acquired in constructing a Bayesian network. Multiple causes can reinforce each other in producing the effect or can undermine the impact of each other. Most existing causal models do not consider their interactions from the perspective of reinforcement or undermining. We show that none of them can represent both interactions. We present the first explicit causal model that can encode both reinforcement and undermining and we show how to use such a model to support efficient probability elicitation.

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“…..} be a partition of a set X of uncertain causes of effect e and sets in R satisfy Eqns (1) and (2). Then, interaction among sets of causes in R is reinforcing.…”

Section: Lemma 1 Letmentioning

confidence: 99%

“…Noisy-MAX model [1] becomes noisy-OR model when variables are binary. Therefore, from Lemma 1, when domain is binary, noisy-MAX represents only reinforcing interaction.…”

Section: Related Models Of Causal Interactionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling Causal Reinforcement and Undermining with Noisy-AND Trees

Xiang

Jia

2006

Advances in Artificial Intelligence

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“…This can be a considerable task if a large number of probabilities have to be elicited [19]. Fortunately, techniques have been developed that can simplify this process, for instance the noisy-OR gate and its generalizations [23], [24], [11]. The number of probabilities to be assessed with such a technique is linear rather than exponential in the number of parents.…”

Section: Knowledge Elicitationmentioning

confidence: 99%

A methodology for developing local smart diagnostic models using expert knowledge

Madsen

Søndberg-Jeppesen

Lohse

et al. 2015

2015 IEEE 13th International Conference on Industrial Informatics (INDIN)

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Abstract-This paper describes an innovative modular component-based modelling approach for diagnostics and condition-monitoring of manufacturing equipment. The approach is based on the use of object-oriented Bayesian networks, which supports a natural decomposition of a large and complex system into a set of less complex components. The methodology consists of six steps supporting the development process: Begin, Design, Implement, Test, Analyse, and Deploy. The process is iterative and the steps should be repeated until a satisfactory model has been achieved. The paper describes the details of the methodology as well as illustrates the use of the componentbased modelling approach on a linear axis used in manufacturing. This application demonstrates the power and flexibility of the approach for diagnostics and condition-monitoring and shows a significant potential of the approach for modular componentbased modelling in manufacturing and other domains.

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“…A leaky probability captures the probability of occurrence of the effect when all explicitly represented causes are absent. Diez [1] and Srinivas [15] also studied generalization of the noisy-OR model. Diez [1] the noisy-MIN models.…”

Section: Introductionmentioning

confidence: 99%

Modeling Causal Reinforcement and Undermining for Efficient CPT Elicitation

Xiang

Jia

2007

IEEE Trans. Knowl. Data Eng.

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Abstract-Representation of uncertain knowledge using a Bayesian network requires acquisition of a conditional probability table (CPT) for each variable. The CPT can be acquired by data mining or elicitation. When data are insufficient to support mining, causal modeling, such as the noisy-OR, aids elicitation by reducing the number of probability parameters to be acquired from human experts. Multiple causes can reinforce each other in producing the effect or can undermine the impact of each other. Most existing causal models do not consider causal interactions from the perspective of reinforcement or undermining. Our analysis shows that none can represent both interactions. Except the RNOR, other models also limit parameters to probabilities of single-cause events. We present the first general causal model, the non-impeding noisy-AND tree, that allows encoding of both reinforcement and undermining. It supports efficient CPT acquisition by elicitating a partial ordering of causes in terms of a tree topology, plus necessary numerical parameters. It also allows incorporation of probabilities for multi-cause events.

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Parameter adjustment in Bayes networks. The generalized noisy OR–gate

Cited by 147 publications

References 10 publications

Modeling Causal Reinforcement and Undermining with Noisy-AND Trees

Modeling Causal Reinforcement and Undermining with Noisy-AND Trees

A methodology for developing local smart diagnostic models using expert knowledge

Modeling Causal Reinforcement and Undermining for Efficient CPT Elicitation

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