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--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.
Table of Contents
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.
462 peges, Paperback
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