Introduction to Graphical Modelling |
 | Author: David Edwards
Publisher: Springer
List Price: $119.00
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Seller: sweethomeliquid2
Rating:  1 reviews
Sales Rank: 586630
Media: Hardcover
Edition: 2nd
Pages: 333
Number Of Items: 1
Shipping Weight (lbs): 1.6
Dimensions (in): 10 x 7.1 x 1
ISBN: 0387950540
Dewey Decimal Number: 519.538
EAN: 9780387950549
ASIN: 0387950540
Publication Date: June 15, 2000
Availability: Usually ships in 1-2 business days
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Product Description
Graphic modelling is a form of multivariate analysis that uses graphs to represent models. These graphs display the structure of dependencies, both associational and causal, between the variables in the model. This textbook provides an introduction to graphical modelling with emphasis on applications and practicalities rather than on a formal development. It is bsed on the popular software package for graphical modelling, MIM, a freeware version of which can be downloaded from the Internet. Following an introductory chapter which sets the scene and describes 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 the second edition. Chapter 7 describes the use of directed graphs, chain graphs, and other graphs. Chapter 8 summarizes some recent work on causal inference, relevant when graphical models are given a causal interpretation. This book will provide a useful introduction to this topic for students and researchers.
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Customer Reviews:
directed graphs, path analysis and causality not the common statistical graphics February 17, 2008
Michael R. Chernick (Holland PA)
30 out of 30 found this review helpful
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.
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