An importance sampling approach to integrate expert knowledge when learning Bayesian networks from data

Authors:
Andrés Cano;Andrés R. Masegosa;Serafín Moral
Affiliations:
Department of Computer Science and A.I., University of Granada, Spain;Department of Computer Science and A.I., University of Granada, Spain;Department of Computer Science and A.I., University of Granada, Spain
Venue:
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Year:
2010

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Abstract

The introduction of expert knowledge when learning Bayesian Networks from data is known to be an excellent approach to boost the performance of automatic learning methods, specially when the data is scarce. Previous approaches for this problem based on Bayesian statistics introduce the expert knowledge modifying the prior probability distributions. In this study, we propose a new methodology based on Monte Carlo simulation which starts with non-informative priors and requires knowledge from the expert a posteriori, when the simulation ends. We also explore a new Importance Sampling method for Monte Carlo simulation and the definition of new non-informative priors for the structure of the network. All these approaches are experimentally validated with five standard Bayesian networks.