9780387950549 - Introduction to Graphical Modelling (Springer Texts in Statistics) von Edwards, David (5 Ergebnisse)

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,900grams, ISBN:9780387950549.

hardcover. Zustand: Very Good.

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,850grams, ISBN:9780387950549.

Hardback. Zustand: Very Good. No Jacket. 2nd 2000. Very good condition with no wrapper. Second Edition. This textbook provides an introduction to graphical modelling with emphasis on applications and practicalities rather than on a formal development. Based on the popular software package for graphical modelling, MIM, a freeware version of which can be downloaded from the internet. Glazed boards. xv and 333 pages including index. Boards faded and a little scuffed. A few minor foxspots to text block. Name in ink to top of front endpaper else contents clean. Packaged with care and promptly dispatched!

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 based 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.