Collazo R.A., Görgen C., Smith J.Q. Chain Event Graphs

Boca Raton, USA: CRC Press, Taylor & Francis Group, LLC., 2018. — 254 p. — (Chapman & Hall/CRC, Computer Science and Data Analysis Series). — ISBN13: 978-1-4987-2960-4.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 packagesBayesian inference using graphs
The Chain Event Graph
Reasoning with a CEG
Estimation and propagation on a given CEG
Model selection for CEGs
How to model with a CEG: A real-world application
Causal inference using CEGs