Graphical modelling is a form of multivariate analysis that uses graphs to represent models. They enable concise representations of associational and casual relations between variables under study. This textbook provides an introduction to graphical models whose emphasis is on its applications and on the practicalities rather than a formal development. with the book comes a diskette containing a student version of MIM - a popular graphical modelling software package for the PC. 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 mixed models for discrete and continuous data. Further chapters cover hypothesis testing for mixed models and discuss issues of model selection and more advanced topics.
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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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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