Bayesian learning of Bayesian networks with informative priors

This paper presents and evaluates an approach to Bayesian model averaging where the models are Bayesian nets (BNs). Prior distributions are defined using stochastic logic programs and the MCMC Metropolis-Hastings algorithm is used to (approximately) sample from the posterior. 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.

• Type: Journal
• Year: 2008
• Where: AMAI
• Authors: Nicos Angelopoulos, James Cussens
• Keywords: AMAI 2008, Artificial Intelligence, Bayesian Model, Bayesian Nets, Stochastic Logic Programs