The application of graph theory to modelling systems began in several scientific areas, among them statistical physics (the study of large particle systems), genetics (studying inheritable properties of natural species), and interactions in contingency tables. The use of graphical models in

statistics has increased considerably in these and other areas such as artificial intelligence, and the theory has been greatly developed and extended. This is the first comprehensive and authoritative account of the theory of graphical models. Written by a leading expert in the field, it contains

the fundamentals graph required and a thorough study of Markov properties associated with various type of graphs, the statistical theory of log-linear and graphical models, and graphical tables with mixed discrete-continuous variables in developed detail. Special topics, such as the application of

graphical models to probabilistic expert systems, are described briefly, and appendices give details of the multivariate normal distribution and of the theory of regular exponential families.