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CIDM
2009
IEEE

A new hybrid method for Bayesian network learning With dependency constraints

9 years 8 months ago
A new hybrid method for Bayesian network learning With dependency constraints
Abstract— A Bayes net has qualitative and quantitative aspects: The qualitative aspect is its graphical structure that corresponds to correlations among the variables in the Bayes net. The quantitative aspects are the net parameters. This paper develops a hybrid criterion for learning Bayes net structures that is based on both aspects. We combine model selection criteria measuring data fit with correlation information from statistical tests: Given a sample d, search for a structure G that maximizes score(G, d), over the set of structures G that satisfy the dependencies detected in d. We rely on the statistical test only to accept conditional dependencies, not conditional independencies. We show how to adapt local search algorithms to accommodate the observed dependencies. Simulation studies with GES search and the BDeu/BIC scores provide evidence that the additional dependency information leads to Bayes nets that better fit the target model in distribution and structure.
Oliver Schulte, Gustavo Frigo, Russell Greiner, We
Added 20 May 2010
Updated 20 May 2010
Type Conference
Year 2009
Where CIDM
Authors Oliver Schulte, Gustavo Frigo, Russell Greiner, Wei Luo, Hassan Khosravi
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