
AI2006, Springer Verlag, LNCS Vol.4304, pp.192203, 2006.
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 artificail data sets.
Hybrid models compare favourably to other local structure learners,
allowing simple representations given limited data
combined with richer representations given massive data.
 Paper:
 [doi:10.1007/11941439_23]['06],
isbn:9783540497875.
 [pdf]['06]

