Separation and completeness properties for AMP chain graph Markov models.

DOI10.1214/aos/1015345961zbMath1043.62080OpenAlexW1512305453MaRDI QIDQ1848925

Michael Levitz, Michael D. Perlman, David Madigan

Publication date: 14 November 2002

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aos/1015345961

zbMATH Keywords

Bayesian networkscompletenessefficient algorithm

Mathematics Subject Classification ID

Applications of graph theory (05C90) Graph theory (including graph drawing) in computer science (68R10) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items

Unnamed Item, Every LWF and AMP Chain Graph Originates from a Set of Causal Models, Joint Response Graphs and Separation Induced by Triangular Systems, Improved evolutionary-based feature selection technique using extension of knowledge based on the rough approximations, Properties of graphical regression models for multidimensional categorical data, Graphical modelling of multivariate time series, Learning AMP chain graphs and some marginal models thereof under faithfulness, Multiple testing and error control in Gaussian graphical model selection, AMP Chain Graphs: Minimal Separators and Structure Learning Algorithms, Marginal AMP chain graphs, A note on faithfulness and total positivity, Characterizing Markov equivalence classes for AMP chain graph models, Unifying Markov properties for graphical models, Minimal sufficient causation and directed acyclic graphs, Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property

Uses Software

• TETRAD

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• BIFROST -- Block recursive models Induced From Relevant knowledge, Observations, and Statistical Techniques
• Graphical models for associations between variables, some of which are qualitative and some quantitative
• Causation, prediction, and search
• Normal linear regression models with recursive graphical Markov structure
• Bayesian analysis in expert systems. With comments and a rejoinder by the authors
• On chain graph models for description of conditional independence structures
• A recovery algorithm for chain graphs
• Learning Bayesian networks: The combination of knowledge and statistical data
• Distinctness of the eigenvalues of a quadratic form in a multivariate sample
• Alternative Markov Properties for Chain Graphs
• Bayesian model averaging and model selection for markov equivalence classes of acyclic digraphs
• Probabilistic Networks and Expert Systems
• Independence properties of directed markov fields
• Identifying independence in bayesian networks