Partial Order MCMC for Structure Discovery in Bayesian Networks
Machine Learning
2012-02-20 v1 Machine Learning
Abstract
We present a new Markov chain Monte Carlo method for estimating posterior probabilities of structural features in Bayesian networks. The method draws samples from the posterior distribution of partial orders on the nodes; for each sampled partial order, the conditional probabilities of interest are computed exactly. We give both analytical and empirical results that suggest the superiority of the new method compared to previous methods, which sample either directed acyclic graphs or linear orders on the nodes.
Cite
@article{arxiv.1202.3753,
title = {Partial Order MCMC for Structure Discovery in Bayesian Networks},
author = {Teppo Niinimaki and Pekka Parviainen and Mikko Koivisto},
journal= {arXiv preprint arXiv:1202.3753},
year = {2012}
}