Partial Order MCMC for Structure Discovery in Bayesian Networks

SPEAKER:
Teppo Niinimäki
University of Helsinki, HIIT

TITLE:
Partial Order MCMC for Structure Discovery in Bayesian Networks

ABSTRACT:
Learning the structure of a Bayesian network from given data is an 
extensively studied problem. 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.

Joint work with Pekka Parviainen and Mikko Koivisto.