Graphical Models in Applied Multivariate Statistics (Hardcover)

J. Whittaker

ISBN 10: 0471917508 / ISBN 13: 9780471917502

Published by John Wiley & Sons Inc, New York, 1990

About this Item

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 our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9780471917502

Bibliographic Details

Title: Graphical Models in Applied Multivariate ...

Publisher: John Wiley & Sons Inc, New York

Publication Date: 1990

Binding: Hardcover

Condition: new

Edition: 1st Edition

About this title

Synopsis:

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.

Synopsis:

The fundamental notion of conditional independence is embedded in the theories of probability and statistics. Recent developments in this field and their importance for describing and modelling multivariate systems are the main areas covered in this book. Standard statistical practice altered radically during the 1970s and the development of log-linear models made it possible to formulate complex models for the dependences between the variables cross-classifying a contingency table. The elucidation of the family of graphical models as a subset of log-linear models unravelled the connection between these models and the fundamental notion of conditional independence. Graphical modelling is that body of statistical techniques based on fitting graphical models to data. It has the advantages of interpretation, simplification and unification. This book is directed at the student who requires a course on applied multivariate statistics unified by the concept of conditional independence, and at the research worker essentially concerned to apply the techniques of graphical modelling.

It contains some exercises and solutions, many numerical examples and a few examples of a substantial practical nature.