Boca Raton, USA: CRC Press, Taylor & Francis Group, LLC., 2018. — 254 p. — (Chapman & Hall/CRC, Computer Science and Data Analysis Series). — ISBN-13 978-1-4987-2960-4.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 packages Contents Introduction Bayesian 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
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