Average Reviews:
(More customer reviews)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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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.
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