Chain Event Graphs
Rodrigo A Collazo author Jim Q Smith author Christiane Goergen author
Format:Paperback
Publisher:Taylor & Francis Ltd
Published:30th Jun '20
Currently unavailable, and unfortunately no date known when it will be back
This paperback is available in another edition too:
- Hardback£86.99(9781498729604)
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 <
"Statisticians Collazo, Görgen, and Smith provide a thorough introduction to the methodology of chain event graphs. The authors present background on discrete statistical modeling and the use of Bayesian inference. The chain event graph method is shown to be less restrictive than that of Bayesian networks, though it represents something of a generalization of that method. Beginning with an event tree, the chain event graph is a graphical representation that can represent a process of developing events. The authors present an array of examples to illustrate the concepts, and exercises are scattered throughout the text. Included with the book's purchase is access to software to create these models. Readers interested in this subject may also wish to consult the works of Judea Pearl, who developed Bayesian Networks and promoted the use of a probabilistic approach to the field of artificial intelligence (see, for example, Causality: Models, Reasoning, and Inference, CH, Mar'10, 47-3771)."
~CHOICE, R. L. Pour, emeritus, Emory and Henry College
Summing Up: Recommended. Upper-division undergraduates through faculty and professionals.
"Statisticians Collazo, Görgen, and Smith provide a thorough introduction to the methodology of chain event graphs. The authors present background on discrete statistical modeling and the use of Bayesian inference. The chain event graph method is shown to be less restrictive than that of Bayesian networks, though it represents something of a generalization of that method. Beginning with an event tree, the chain event graph is a graphical representation that can represent a process of developing events. The authors present an array of examples to illustrate the concepts, and exercises are scattered throughout the text. Included with the book's purchase is access to software to create these models. Readers interested in this subject may also wish to consult the works of Judea Pearl, who developed Bayesian Networks and promoted the use of a probabilistic approach to the field of artificial intelligence (see, for example, Causality: Models, Reasoning, and Inference, CH, Mar'10, 47-3771)."
~CHOICE, R. L. Pour, emeritus, Emory and Henry College
Summing Up: Recommended. Upper-division undergraduates through faculty and professionals.
ISBN: 9780367572310
Dimensions: unknown
Weight: 470g
234 pages