Chain Event Graphs

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

Rodrigo A. Collazo

is a methodological and computational statistician based at the Naval Systems Analysis Centre (CASNAV) in Rio de Janeiro, Brazil. Christiane Görgen is a mathematical statistician at the Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany. Jim Q. Smith is a professor of statistics at the University of Warwick, UK. He has published widely in the field of statistics, AI, and decision analysis and has written two other books, most recently Bayesian Decision Analysis: Principles and Practice (Cambridge University Press 2010).

Table of Contents

Chapter 1. Introduction
Chapter 2. Bayesian inference using graphs
Chapter 3. The Chain Event Graph
Chapter 4. Reasoning with a CEG
Chapter 5. Estimation and propagation on a given CEG
Chapter 6. Model selection for CEGs
Chapter 7. How to model with a CEG: A real-world application
Chapter 8. Causal inference using CEGs