Stratified Gaussian graphical models

H Nyman, Johan Pensar, J Corander

    Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

    2 Citeringar (Scopus)

    Sammanfattning

    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.
    OriginalspråkOdefinierat/okänt
    Sidor (från-till)5556–5578
    Antal sidor23
    TidskriftCommunications in Statistics - Theory and Methods
    Volym46
    Utgåva11
    DOI
    StatusPublicerad - 2017
    MoE-publikationstypA1 Tidskriftsartikel-refererad

    Nyckelord

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

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