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Learning Hybrid Bayesian Networks by MML Rodney T. O'Donnell, Lloyd Allison, and Kevin B. Korb
 

Summary: Learning Hybrid Bayesian Networks by MML
Rodney T. O'Donnell, Lloyd Allison, and Kevin B. Korb
School of Information Technology
Monash University
Clayton, Victoria 3800, Australia
Abstract. We use a Markov Chain Monte Carlo (MCMC) MML algo-
rithm to learn hybrid Bayesian networks from observational data. Hybrid
networks represent local structure, using conditional probability tables
(CPT), logit models, decision trees or hybrid models, i.e., combinations of
the three. We compare this method with alternative local structure learn-
ing algorithms using the MDL and BDe metrics. Results are presented
for both real and artificial data sets. Hybrid models compare favourably
to other local structure learners, allowing simple representations given
limited data combined with richer representations given massive data.
1 Introduction
There is a large literature on methods of learning Bayesian networks from ob-
served data. Much of that work has focused solely on learning network structure,
treating network parameterization as a separate process. However, some work
has been done on learning network structure and parameters simultaneously
and many algorithms exist for performing this task. Most techniques involve a

  

Source: Allison, Lloyd - Caulfield School of Information Technology, Monash University

 

Collections: Computer Technologies and Information Sciences