Bayesian learning of Bayesian networks with informative priors

Research output: Contribution to journalArticle (Academic Journal)

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.
Original languageEnglish
Pages (from-to)53-98
Number of pages46
JournalAnnals of Mathematics and Artificial Intelligence
Volume54
Issue number1-3
DOIs
Publication statusPublished - Nov 2008

Bibliographical note

10.1007/s10472-009-9133-x

Keywords

  • Prior knowledge
  • Bayesian inference
  • Bayesian model averaging
  • Markov chain Monte Carlo
  • Loss functions
  • Stochastic logic programs
  • STOCHASTIC LOGIC PROGRAMS
  • GRAPHICAL MODELS
  • PROBABILISTIC NETWORKS
  • STRUCTURE DISCOVERY
  • KNOWLEDGE

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