Stratified Gaussian graphical models

H Nyman, Johan Pensar, J Corander

    Research output: Contribution to journalArticleScientificpeer-review

    2 Citations (Scopus)

    Abstract

    Gaussian graphical models represent the backbone of the statistical toolbox for analyzing continuous multivariate systems. However, due to the intrinsic properties of the multivariate normal distribution, use of this model family may hide certain forms of context-specific independence that are natural to consider from an applied perspective. Such independencies have been earlier introduced to generalize discrete graphical models and Bayesian networks into more flexible model families. Here, we adapt the idea of context-specific independence to Gaussian graphical models by introducing a stratification of the Euclidean space such that a conditional independence may hold in certain segments but be absent elsewhere. It is shown that the stratified models define a curved exponential family, which retains considerable tractability for parameter estimation and model selection.
    Original languageUndefined/Unknown
    Pages (from-to)5556–5578
    Number of pages23
    JournalCommunications in Statistics - Theory and Methods
    Volume46
    Issue number11
    DOIs
    Publication statusPublished - 2017
    MoE publication typeA1 Journal article-refereed

    Keywords

    • Context-specific independence
    • Multivariate normal distribution
    • Bayesian model learning
    • Gaussian graphical model

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