Bayesian learning of Bayesian networks with informative priors

Nicos Angelopoulos, James Cussens
2008 Annals of Mathematics and Artificial Intelligence  
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
more » ... 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.
doi:10.1007/s10472-009-9133-x fatcat:yahm6moxr5d2fnzfdh4muusohq