Linear dependencies represented by chain graphs. With comments and a rejoinder by the authors
From MaRDI portal
Publication:1596072
DOI10.1214/ss/1177010887zbMath0955.62593OpenAlexW1964356176WikidataQ114599119 ScholiaQ114599119MaRDI QIDQ1596072
Publication date: 7 February 2001
Published in: Statistical Science (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1214/ss/1177010887
Related Items
Statistics and causal inference: A review. (With discussion), Scaling it up: stochastic search structure learning in graphical models, Every LWF and AMP Chain Graph Originates from a Set of Causal Models, Marginalization and conditioning for LWF chain graphs, Independence in multi-way contingency tables: S.N. Roy's breakthroughs and later develop\-ments, A characterization of Markov equivalence classes for acyclic digraphs, Partial inversion for linear systems and partial closure of independence graphs, On the relation between conditional independence models determined by finite distributive lattices and by directed acyclic graphs, Chain graphs for multilevel models, Estimating linear covariance models with numerical nonlinear algebra, Covariance chains, A note on marginal and conditional independence, The geometry of Gaussian double Markovian distributions, Graphical modeling of stochastic processes driven by correlated noise, Sequences of regressions and their independences, Analysing Multivariate Spatial Point Processes with Continuous Marks: A Graphical Modelling Approach, On expressiveness of the chain graph interpretations, On finite exchangeability and conditional independence, Wishart distributions for decomposable covariance graph models, Properties of graphical regression models for multidimensional categorical data, Stable mixed graphs, Chain graph models of multivariate regression type for categorical data, Probability distributions with summary graph structure, Multiple testing and error control in Gaussian graphical model selection, AMP Chain Graphs: Minimal Separators and Structure Learning Algorithms, Robust concentration graph model selection, Marginal AMP chain graphs, Consistent model selection based on parameter estimates., Markov properties for mixed graphs, Binary Models for Marginal Independence, Order-Independent Structure Learning of Multivariate Regression Chain Graphs, Maximum pairwise Bayes factors for covariance structure testing, A decomposition-based algorithm for learning the structure of multivariate regression chain graphs, Derived variables calculated from similar joint responses: some characteristics and examples, From association to causation via regression, Probabilistic Graphical Models and Markov Networks, Causal inference in statistics: an overview, On a hypergraph probabilistic graphical model, Bayesian inference for graphical factor analysis models, On generating random Gaussian graphical models, Context-specific independencies in stratified chain regression graphical models, Conjugate and conditional conjugate Bayesian analysis of discrete graphical models of marginal independence, Unifying Markov properties for graphical models, Symmetry and lattice conditional independence in a multivariate normal distribution, A graphical characterization of the largest chain graphs, A review of Gaussian Markov models for conditional independence, Normal linear regression models with recursive graphical Markov structure, Probability based independence sampler for Bayesian quantitative learning in graphical log-linear marginal models, Faithfulness of Probability Distributions and Graphs, The construction of multivariate distributions from Markov random fields, Bayesian Structure Learning in Multilayered Genomic Networks, Graphical Models for Processing Missing Data, Customized structural elicitation, Log‐mean Linear Parameterization for Discrete Graphical Models of Marginal Independence and the Analysis of Dichotomizations, Iterative proportional fitting for nonhierarchical log-linear models, Chain graph interpretations and their relations revisited
