INTRODUCTION TO GRAPHICAL MODELLING (SPRINGER TEXTS IN STATISTICS)

Edwards, David

ISBN 10: 0387950540 / ISBN 13: 9780387950549

Edité par Springer-Verlag, 2000

A propos de cet article

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!

N° de réf. du vendeur 1830327

Détails bibliographiques

• Titre: INTRODUCTION TO GRAPHICAL MODELLING (SPRINGER TEXTS IN STATISTICS)
• Éditeur: Springer-Verlag
• Date de parution: 2000
• ISBN à 10 chiffres: 0387950540
• ISBN à 13 chiffres: 9780387950549
• Reliure: Hardback
• État de l'article: Very Good
• État de la jaquette: No Jacket

A propos de ce titre

Synopsis

Graphical models are of great interest in statistics and computer science. This book is more oriented towards applications than other books on this subject, and it will be of interest to researchers and graduate students in both of these areas.

Présentation de l'éditeur

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.