Graphical Models in Applied Multivariate Statistics

The Wiley Paperback Series makes valuable content more accessible to a new generation of statisticians, mathematicians and scientists.

Graphical models--a subset of log-linear models--reveal the interrelationships between multiple variables and features of the underlying conditional independence. This introduction to the use of graphical models in the description and modeling of multivariate systems covers conditional independence, several types of independence graphs, Gaussian models, issues in model selection, regression and decomposition. Many numerical examples and exercises with solutions are included.

This book is aimed at students who require a course on applied multivariate statistics unified by the concept of conditional independence and researchers concerned with applying graphical modelling techniques.

Independence and Interaction.

Independence Graphs.

Information Divergence.

The Inverse Variance.

Graphical Gaussian Models.

Graphical Log-Linear Models.

Model Selection.

Methods for Sparse Tables.

Regression and Graphical Chain Models.

Models for Mixed Variables.

Decompositions and Decomposability.

Appendices.

References.

Author Index.

Subject Index.

Joe Whittaker is the author of Graphical Models in Applied Multivariate Statistics, published by Wiley. The Wiley Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians and scientists.

Graphical Models in Applied Multivariate Statistics

Joe Whittaker

Department of Mathematics, University of Lancaster, UK.

Graphical models--a subset of log-linear models--reveal the interrelationships between multiple variables and features of the underlying conditional independence. This introduction to the use of graphical models in the description and modeling of multivariate systems covers conditional independence, several types of independence graphs, Gaussian models, issues in model selection, regression and decomposition. Many numerical examples and exercises with solutions are included.

This book is aimed at students who require a course on applied multivariate statistics unified by the concept of conditional independence and researchers concerned with applying graphical modelling techniques.