Journal ArticleDOI
A Method for Integrating Expert Knowledge When Learning Bayesian Networks From Data
Andrés Cano,Andrés R. Masegosa,Serafín Moral +2 more
- Vol. 41, Iss: 5, pp 1382-1394
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TLDR
This paper presents a new methodology for integrating expert knowledge, based on Monte Carlo simulations and which avoids the costly elicitation of these prior distributions and only requests from the expert information about those direct probabilistic relationships between variables which cannot be reliably discerned with the help of the data.Abstract:
Automatic learning of Bayesian networks from data is a challenging task, particularly when the data are scarce and the problem domain contains a high number of random variables. The introduction of expert knowledge is recognized as an excellent solution for reducing the inherent uncertainty of the models retrieved by automatic learning methods. Previous approaches to this problem based on Bayesian statistics introduce the expert knowledge by the elicitation of informative prior probability distributions of the graph structures. In this paper, we present a new methodology for integrating expert knowledge, based on Monte Carlo simulations and which avoids the costly elicitation of these prior distributions and only requests from the expert information about those direct probabilistic relationships between variables which cannot be reliably discerned with the help of the data.read more
Citations
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Review: Bayesian networks in environmental modelling
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Decision support analysis for safety control in complex project environments based on Bayesian Networks
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Bayesian Network Approach to Multinomial Parameter Learning using Data and Expert Judgments
TL;DR: A multinomial parameter learning method is presented, which can easily incorporate both expert judgments and data during the parameter learning process and achieves much greater learning accuracy with much less data.
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Bayesian network modeling of accident investigation reports for aviation safety assessment
Xiaoge Zhang,Sankaran Mahadevan +1 more
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Journal ArticleDOI
An interactive approach for Bayesian network learning using domain/expert knowledge
Andrés R. Masegosa,Serafín Moral +1 more
TL;DR: The interactive approach for integrating domain/expert knowledge at different stages of the learning process of a Bayesian network: while learning the skeleton and when directing the edges of the directed acyclic graph structure.
References
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TL;DR: The authors axiomatize the connection between causal structure and probabilistic independence, explore several varieties of causal indistinguishability, formulate a theory of manipulation, and develop asymptotically reliable procedures for searching over equivalence classes of causal models.
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Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
TL;DR: In this article, a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data is presented, which is derived from a set of assumptions made previously as well as the assumption of likelihood equivalence, which says that data should not help to discriminate network structures that represent the same assertions of conditional independence.
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A Bayesian Method for the Induction of Probabilistic Networks from Data
TL;DR: This paper presents a Bayesian method for constructing probabilistic networks from databases, focusing on constructing Bayesian belief networks, and extends the basic method to handle missing data and hidden variables.
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Learning Bayesian networks
TL;DR: This chapter discusses Bayesian Networks, a framework for Bayesian Structure Learning, and some of the algorithms used in this framework.