Probability distributions with summary graph structure

DOI10.3150/10-BEJ309zbMath1245.62062arXiv1003.3259MaRDI QIDQ638759

Nanny Wermuth

Publication date: 14 September 2011

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1003.3259

zbMATH Keywords

directed acyclic graphtriangular systemconcentration graphcovariance graphregression graphindependence graphdependence confoundinggraphical Markov model

Mathematics Subject Classification ID

Characterization and structure theory for multivariate probability distributions; copulas (62H05) Applications of graph theory (05C90) Directed graphs (digraphs), tournaments (05C20)

Related Items

Marginalization and conditioning for LWF chain graphs, Nested Markov properties for acyclic directed mixed graphs, Causal structure learning: a combinatorial perspective, Sequences of regressions and their independences, Comments on: Sequences of regressions and their independencies, Nested covariance determinants and restricted trek separation in Gaussian graphical models, Stable mixed graphs, Markovian acyclic directed mixed graphs for discrete data, Markov properties for mixed graphs, Computation of maximum likelihood estimates in cyclic structural equation models, Smooth, identifiable supermodels of discrete DAG models with latent variables, Global identifiability of linear structural equation models, Half-trek criterion for generic identifiability of linear structural equation models, Unifying Markov properties for graphical models, A review of Gaussian Markov models for conditional independence, Graphs for Margins of Bayesian Networks, Faithfulness of Probability Distributions and Graphs, Comments on: Sequences of regressions and their independences, On the causal interpretation of acyclic mixed graphs under multivariate normality

Uses Software

• TETRAD

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