Introduction to Graphical Modelling [Hardcover]

David Edwards (Author)

Book Description

ISBN-10: 0387950540 | ISBN-13: 978-0387950549 | Publication Date: June 15, 2000 | Edition: 2nd

A useful introduction to this topic for both students and researchers, with an emphasis on applications and practicalities rather than on a formal development. It is based on the popular software package for graphical modelling, MIM, freely available for downloading from the Internet. Following a description of some of the basic ideas of graphical modelling, subsequent chapters describe particular families of models, including log-linear models, Gaussian models, and models for mixed discrete and continuous variables. Further chapters cover hypothesis testing and model selection. Chapters 7 and 8 are new to this second edition and describe the use of directed, chain, and other graphs, complete with a summary of recent work on causal inference.

Editorial Reviews

Review

From the reviews:

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

"This is a valuable book that should increase in value over time. It seems clear that in the future, statisticians will need to deal with larger, more complicated collections of data…Any statistician who is planning to tackle the changing nature of data collection in the 21^st Century should know about graphical models. This book provides a great place to begin learning about them."

SIAM REVIEW

"…this is an important book for all concerned with the statistical analysis of multivariate data such as arise particularly, but not only, in observational studies in the medical and social sciences. In a broader context it gives a thoughtful introduction to an active topic of current research."

TECHNOMETRICS

"This book’s strength is its accessibility. Numerous illustrations and example datasets are well integrated with the text…The examples are well chosen; I was particularly pleased that the author clearly treated datasets as interesting in their own right, not simply as a foil for demonstrating techniques…Edwards presents a clear, engaging introduction to graphical modeling that is very suitable as a first text and should stimulate readers to explore and use this methodology for their own data."

Product Details

• Hardcover: 333 pages
• Publisher: Springer; 2nd edition (June 15, 2000)
• Language: English
• ISBN-10: 0387950540
• ISBN-13: 978-0387950549
• Product Dimensions: 10 x 7.1 x 1 inches
• Shipping Weight: 1.6 pounds (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #1,969,794 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

30 of 30 people found the following review helpful:

4.0 out of 5 stars directed graphs, path analysis and causality not the common statistical graphics, February 16, 2008

By 

Michael R. Chernick "statman31147" (Holland PA) - See all my reviews
(REAL NAME)   

This review is from: Introduction to Graphical Modelling (Hardcover)

Because graphic methods are very popular in statistics, when you read the title you might think this is a book on the use of graphics in statistics. That is not what the book is about. The directed graph on the cover might be a hint for some.

The book deals with the theory of undirected and directed graphs which has applications to causal modeling in statistics and the development of expert systems (which Edwards claim are now more commonly referred to as probabilistic networks).

This subject is being made popular again based on the recent work of Edwards, Pearl, Rubin and a few others. The book incorporate the approach in many classical statistical problems. This is not commonly seen except in specialized texts on latent variable models.

Edwards discusses implementation of the methods with the freeware MIMS that is available in Denmark and on the web. The book is very well written and applications in MIMS are given throughout the text. Edwards also provides us with an excellent list of references (over 200 with many on causal modeling).

The software LISREL produced by researchers in the US at UCLA for latent variable and path analyses is only briefly mentioned on page 217. The lack of coverage of American and British publications on this topic is the only drawback I see.