Open Access
December 2017 Marginal Pseudo-Likelihood Learning of Discrete Markov Network Structures
Johan Pensar, Henrik Nyman, Juha Niiranen, Jukka Corander
Bayesian Anal. 12(4): 1195-1215 (December 2017). DOI: 10.1214/16-BA1032


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.


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Johan Pensar. Henrik Nyman. Juha Niiranen. Jukka Corander. "Marginal Pseudo-Likelihood Learning of Discrete Markov Network Structures." Bayesian Anal. 12 (4) 1195 - 1215, December 2017.


Published: December 2017
First available in Project Euclid: 31 October 2016

zbMATH: 1384.62178
MathSciNet: MR3724983
Digital Object Identifier: 10.1214/16-BA1032

Keywords: Bayesian inference , Markov networks , non-chordal graph , pseudo-likelihood , regularization , structure learning

Vol.12 • No. 4 • December 2017
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