High-dimensional structure learning of binary pairwise Markov networks: A comparative numerical study

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • University of Helsinki
  • University of Oslo
  • Tokyo Institute of Technology
  • Wellcome Sanger Institute

Abstract

Learning the undirected graph structure of a Markov network from data is a problem that has received a lot of attention during the last few decades. As a result of the general applicability of the model class, a myriad of methods have been developed in parallel in several research fields. Recently, as the size of the considered systems has increased, the focus of new methods has been shifted towards the high-dimensional domain. In particular, introduction of the pseudo-likelihood function has pushed the limits of score-based methods which were originally based on the likelihood function. At the same time, methods based on simple pairwise tests have been developed to meet the challenges arising from increasingly large data sets in computational biology. Apart from being applicable to high-dimensional problems, methods based on the pseudo-likelihood and pairwise tests are fundamentally very different. To compare the accuracy of the different types of methods, an extensive numerical study is performed on data generated by binary pairwise Markov networks. A parallelizable Gibbs sampler, based on restricted Boltzmann machines, is proposed as a tool to efficiently sample from sparse high-dimensional networks. The results of the study show that pairwise methods can be more accurate than pseudo-likelihood methods in settings often encountered in high-dimensional structure learning applications.

Details

Original languageEnglish
Pages (from-to)62-76
Number of pages15
JournalComputational Statistics and Data Analysis
Volume141
Publication statusPublished - 1 Jan 2020
MoE publication typeA1 Journal article-refereed

    Research areas

  • Gibbs sampler, Ising model, Markov network, Mutual information, Pseudo-likelihood, Structure learning

ID: 35440622