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Bayesian learning of Bayesian networks with informative priors

Angelopoulos, Nicos and Cussens, James 2009. Bayesian learning of Bayesian networks with informative priors. Annals of Mathematics and Artificial Intelligence 54 , pp. 53-98. 10.1007/s10472-009-9133-x

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Abstract

This paper presents and evaluates an approach to Bayesian model averaging where the models are Bayesian nets (BNs). A comprehensive study of the literature on structural priors for BNs is conducted. A number of prior distributions are defined using stochastic logic programs and the MCMC Metropolis-Hastings algorithm is used to (approximately) sample from the posterior. We use proposals which are tightly coupled to the priors which give rise to cheaply computable acceptance probabilities. Experiments using data generated from known BNs have been conducted to evaluate the method. The experiments used 6 different BNs and varied: the structural prior, the parameter prior, the Metropolis-Hasting proposal and the data size. Each experiment was repeated three times with different random seeds to test the robustness of the MCMC-produced results. Our results show that with effective priors (i) robust results are produced and (ii) informative priors improve results significantly.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Medicine
Publisher: Springer Verlag (Germany)
ISSN: 1012-2443
Date of Acceptance: 19 March 2008
Last Modified: 06 Aug 2020 14:00
URI: http://orca.cf.ac.uk/id/eprint/134002

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