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2008
Springer

Bayesian learning of Bayesian networks with informative priors

8 years 5 months ago
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.
Nicos Angelopoulos, James Cussens
Added 08 Dec 2010
Updated 08 Dec 2010
Type Journal
Year 2008
Where AMAI
Authors Nicos Angelopoulos, James Cussens
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