Henrik Nyman (Åbo Akademi University): Stratified Gaussian Graphical Models

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. We will present a class of models that incorporates 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. Additionally, a non-reversible MCMC approach is used to infer the model structure best suited to represent the dependence structure found in a given dataset.

Joint work with Johan Pensar and Jukka Corander.

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