Description
Book SynopsisThe idea of modelling systems using graph theory has its origin in several scientific areas: in statistical physics (the study of large particle systems), in genetics (studying inheritable properties of natural species), and in interactions in contingency tables. The use of graphical models in statistics has increased considerably over recent years and the theory has been greatly developed and extended. This book provides the first comprehensive and authoritative account of the theory of graphical models and is written by a leading expert in the field. It contains the fundamental graph theory required and a thorough study of Markov properties associated with various type of graphs. The statistical theory of log-linear and graphical models for contingency tables, covariance selection models, and graphical models with mixed discrete-continous variables in developed detail. Special topics, such as the application of graphical models to probabilistic expert systems, are described briefly,
Trade Review...this is an excellent reference book for those who are interested in studying the mathematical theory for graphical models. * Short Book Reviews, vol. 18, no. 1, April 1998 *
Table of ContentsIntroduction ; 1. Graphs and Hypergraphs ; 2. Conditional Independence and Markov Properties ; 3. Contingency Tables ; 4. Multivariate Normal Models ; 5. Models for Mixed Data ; 6. Further topics ; A Various Prerequisites ; B Linear Algebra and Random Vectors ; C The Multivariate Distribution ; D Exponential Models
