Structure Learning of Contextual Markov Networks using Marginal Pseudo-likelihood

Johan Pensar, H Nyman, J Corander

    Research output: Contribution to journalArticleScientificpeer-review

    Abstract

    Markov networks are popular models for discrete multivariate systems where the dependence structure of the variables is specified by an undirected graph. To allow for more expressive dependence structures, several generalizations of Markov networks have been proposed. Here, we consider the class of contextual Markov networks which takes into account possible context-specific independences among pairs of variables. Structure learning of contextual Markov networks is very challenging due to the extremely large number of possible structures. One of the main challenges has been to design a score, by which a structure can be assessed in terms of model fit related to complexity, without assuming chordality. Here, we introduce the marginal pseudo-likelihood as an analytically tractable criterion for general contextual Markov networks. Our criterion is shown to yield a consistent structure estimator. Experiments demonstrate the favourable properties of our method in terms of predictive accuracy of the inferred models.
    Original languageUndefined/Unknown
    Pages (from-to)455–479
    Number of pages25
    JournalScandinavian Journal of Statistics
    Volume44
    Issue number2
    DOIs
    Publication statusPublished - 2017
    MoE publication typeA1 Journal article-refereed

    Keywords

    • Bayesian inference
    • graphical models
    • Markov networks
    • pseudo-likelihood
    • structure learning
    • Context-specific independence

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