On conditional independence and log-convexity

DOI10.1214/11-AIHP431zbMath1253.62036OpenAlexW2053997885MaRDI QIDQ1930658

František Matúš

Publication date: 11 January 2013

Published in: Annales de l'Institut Henri Poincaré. Probabilités et Statistiques (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aihp/1353098443

zbMATH Keywords

positive definite matriceslog-convexitycontingency tablesconditional independenceMarkov propertiesHammersley-Clifford theoremgraphical Markov modelsMarkov fieldsmultivariate Gaussian distributionsfactorizable distributionscovariance selection modelGibbs potentialsGibbs-Markov equivalence

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Graphs and linear algebra (matrices, eigenvalues, etc.) (05C50) Contingency tables (62H17) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (3)

The geometry of Gaussian double Markovian distributions ⋮ Mutual conditional independence and its applications to model selection in Markov networks ⋮ Contribution of Frantisek Matus to the research on conditional independence

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