Structure Learning of Contextual Markov Networks using Marginal Pseudo-likelihood

Johan Pensar, H Nyman, J Corander

    Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

    5 Citeringar (Scopus)


    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.
    Sidor (från-till)455–479
    Antal sidor25
    TidskriftScandinavian Journal of Statistics
    StatusPublicerad - 2017
    MoE-publikationstypA1 Tidskriftsartikel-refererad


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

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