Graphical Models in Applied Multivariate Statistics (Hardcover)

J. Whittaker

ISBN 10: 0471917508 / ISBN 13: 9780471917502

Edité par John Wiley & Sons Inc, New York, 1990

A propos de cet article

Hardcover. Graphical models--a subset of log-linear models--reveal the interrelationships between multiple variables and features of the underlying conditional independence. Following the theorem-proof-remarks format, 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. Graphical models----a subset of log--linear models----reveal the interrelationships between multiple variables and features of the underlying conditional independence. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9780471917502

Détails bibliographiques

Titre : Graphical Models in Applied Multivariate ...

Éditeur : John Wiley & Sons Inc, New York

Date d'édition : 1990

Reliure : Hardcover

Etat : new

Edition : Edition originale

A propos de ce titre

Synopsis :

Book by Whittaker J

Quatrième de couverture:

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.