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Zusatztext "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. Informationen zum Autor Rodrigo A. Collazo! Christiane Goergen! Jim Q. Smith Zusammenfassung ?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 treefocusses on illustrating inferential techniques! making its methodology accessible to a very broad audience and! most importantly! to practitionersillustrated by a wide range of examples! encompassing important present and future applicationsincludes exercises to test comprehension and can easily be used as a course bookintroduces relevant software packagesRodrigo 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). Inhaltsverzeichnis 1.Introduction Some motivation Why event trees? Using event trees to describe populations How we have arranged the material in this book Exercises 2.Bayesian inference using graphs Inference on discrete statistical models Two common sampling mass functions Two prior-to-posterior analyses Poisson-Gamma and Multinomial-Dirichlet MAP model selection using Bayes Factors Statistical models and structural hypotheses An example of competing models The parametric statistical model Discrete Bayesian networks Factorisations of probability mass functions The d-separation theorem assumptions Estimating probabilities in a BN Propagating probabilities in a BN Concluding remarks Exercises 3.The Chain Event Graph Models represented by tree graphs Probability trees Staged trees The semantics of the Chain Event Graph Comparison of stratified CEGs with Examples of CEG semantics The saturated CEG The simple CEG The square-free CEG Some related structures Exercises 4.Reasoning with a CEG Encoding qualitat...
