Chain Event Graphs

A chain event graph (CEG) is an important generalization of the Bayesian Network (BN). BNs have been extremely useful for modeling discrete processes. However, they are not appropriate for all applications. Over the past six years or so, teams of researchers led by Jim Smith have established a strong theoretical underpinning for CEGs. This book

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Written by some major contributors to the development of this class of graphical models, Chain Event Graphs introduces a viable and straightforward new tool for statistical inference, model selection and learning techniques. The book extends established technologies used in the study of discrete Bayesian Networks so that they apply in a much more general setting

As the first book on Chain Event Graphs, this monograph is expected to become a landmark work on the use of event trees and coloured probability trees in statistics, and to lead to the increased use of such tree models to describe hypotheses about how events might unfold.

Features:



introduces a new and exciting discrete graphical model based on an event tree



focusses on illustrating inferential techniques, making its methodology accessible to a very broad audience and, most importantly, to practitioners



illustrated by a wide range of examples, encompassing important present and future applications



includes exercises to test comprehension and can easily be used as a course book



introduces relevant software packages