K.B.: Learning hybrid Bayesian networks by MML
| Venue: | Proc. 19th Australian Joint Conf. on AI, LNAI (2006 |
| Citations: | 1 - 1 self |
BibTeX
@INPROCEEDINGS{O’donnell_k.b.:learning,
author = {Rodney T. O’donnell and Lloyd Allison and Kevin B. Korb},
title = {K.B.: Learning hybrid Bayesian networks by MML},
booktitle = {Proc. 19th Australian Joint Conf. on AI, LNAI (2006},
year = {}
}
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Abstract
Abstract. We use a Markov Chain Monte Carlo (MCMC) MML algorithm 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 learning 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
