Learning Bayesian networks for discrete data

Bayesian networks have received much attention in the recent literature. In this article,

we propose an approach to learn Bayesian networks using the stochastic approximation

Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses

the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered

by conventional MCMC simulation-based approaches in learning Bayesian networks.

Secondly, it falls into the class of dynamic importance sampling algorithms; the network

features can be inferred by dynamically weighted averaging the samples generated in the

learning process, and the resulting estimates can have much lower variation than the single

model-based estimates. The numerical results indicate that our approach can mix much

faster over the space of Bayesian networks than the conventional MCMC simulation-based

approaches.