论文信息 - Graphical Chain Models

Graphical Chain Models

This article reviews the early twentieth century origins of graphical models and outlines the subsequent major developments from path analysis and conditional independence to the use of graphical models in representing relations among random variables, and, more recently, chains of such multivariate relations. Keywords: conditional independence; chain graphs; multivariate statistics; path analysis; structural equation models

Nanny Wermuth | N. Wermuth

[1] Nanny Wermuth,et al. Graphical models: overview , 2001 .

[2] A. Dobra. Markov bases for decomposable graphical models , 2003 .

[3] N. Wermuth,et al. Causality: a Statistical View , 2004 .

[4] Elena Stanghellini,et al. Identification of a single-factor model using graphical Gaussian rules , 1997 .

[5] J. Koster. On the Validity of the Markov Interpretation of Path Diagrams of Gaussian Structural Equations Systems with Correlated Errors , 1999 .

[6] Steffen L. Lauritzen,et al. Graphical Models for Genetic Analyses , 2003 .

[7] J. Pearl. Graphs, Causality, and Structural Equation Models , 1998 .

[8] Rainer Dahlhaus,et al. Partial correlation analysis for the identification of synaptic connections , 2003, Biological Cybernetics.

[9] P. Spirtes,et al. Ancestral graph Markov models , 2002 .

[10] R. Dahlhaus. Graphical interaction models for multivariate time series1 , 2000 .

[11] D. Lindley. Seeing and Doing: the Concept of Causation , 2002 .

[12] N. Wermuth,et al. Joint response graphs and separation induced by triangular systems , 2004 .

[13] Michel Grzebyk,et al. On identification of multi-factor models with correlated residuals , 2004 .

[14] Roland Fried,et al. Decomposability and selection of graphical models for multivariate time series , 2003 .