Bayesian learning of Bayesian networks with informative priors

Authors:
Nicos Angelopoulos;James Cussens
Affiliations:
School of Biological Sciences, University of Edinburgh, Edinburgh, UK EH9 3JR;Department of Computer Science & York Centre for Complex Systems Analysis, University of York, York, UK YO10 5DD
Venue:
Annals of Mathematics and Artificial Intelligence
Year:
2008

Citing 19
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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.