Marginal Pseudo-Likelihood Learning of Discrete Markov Network Structures

A1 Journal article (refereed)

Internal Authors/Editors

Publication Details

List of Authors: Johan Pensar, Henrik Nyman, Juha Niiranen, Jukka Corander
Publication year: 2016
Journal: Bayesian Analysis
Start page: 1
End page: 21
eISSN: 1931-6690


Markov networks are a popular tool for modeling multivariate distributions over a set of discrete variables. The core of the Markov network representation is an undirected graph which elegantly captures the dependence structure over the variables. Traditionally, the Bayesian approach of learning the graph structure from data has been done under the assumption of chordality since non-chordal graphs are difficult to evaluate for likelihood-based scores. Recently, there has been a surge of interest towards the use of regularized pseudo-likelihood methods as such approaches can avoid the assumption of chordality. Many of the currently available methods necessitate the use of a tuning parameter to adapt the level of regularization for a particular dataset. Here we introduce the marginal pseudo-likelihood which has a built-in regularization through marginalization over the graph-specific nuisance parameters. We prove consistency of the resulting graph estimator via comparison with the pseudo-Bayesian information criterion. To identify high-scoring graph structures in a high-dimensional setting we design a two-step algorithm that exploits the decomposable structure of the score. Using synthetic and existing benchmark networks, the marginal pseudo-likelihood method is shown to perform favorably against recent popular structure learning methods.


Bayesian model learning, Markov models

Last updated on 2019-17-10 at 04:39