Details
Bayesian Theory
, Band 405 1. Aufl.
68,99 € 

Verlag:  Wiley 
Format:  
Veröffentl.:  25.09.2009 
ISBN/EAN:  9780470317716 
Sprache:  englisch 
Anzahl Seiten:  608 
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Beschreibungen
This highly acclaimed text, now available in paperback, provides a thorough account of key concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Informationtheoretic concepts play a central role in the development of the theory, which provides, in particular, a detailed discussion of the problem of specification of socalled prior ignorance . The work is written from the authors s committed Bayesian perspective, but an overview of nonBayesian theories is also provided, and each chapter contains a wideranging critical reexamination of controversial issues. The level of mathematics used is such that most material is accessible to readers with knowledge of advanced calculus. In particular, no knowledge of abstract measure theory is assumed, and the emphasis throughout is on statistical concepts rather than rigorous mathematics. The book will be an ideal source for all students and researchers in statistics, mathematics, decision analysis, economic and business studies, and all branches of science and engineering, who wish to further their understanding of Bayesian statistics
Foundations. Generalisations. Modelling. Inference. Remodelling. Appendices. References. Indexes.
"an excellent primary source for those who wish to learn about the learning and decision process in a situation of uncertainty..." (Measurement Science Technology, February 2001) "an ideal source for all students and researchers in statistics mathematics, decision analysis, economic and business studies and all branches of science and engineering who wish to further their understanding of Bayesian statistics." (Zentralblatt Fur Didaktik der Mathematik) "...Bayesians will find it indispensable: nonBayesians will find, and enjoy, much thoughtprovoking material to challenge their orthodoxy...." (The Statistician, Vol.51, No.2, 2002)
About the Authors Jose M. Bernardo received his PhD from University College London and has subsequently been at the University of Valencia, Spain, where he is currently Professor of Statistics and special scientific advisor to the Governor of the State of Valencia. Adrian F. M. Smith received his PhD from University College London and is currently at Imperial College London, where he is Professor of Statistics and Head of the Department of Mathematics
Bayesian Theory José M. Bernardo Universidad de Valencia, Valencia, Spain Adrian F. M. Smith Imperial College of Science, Technology and Medicine, London, UK Bayesian Theory is the first volume of a related series of three and will be followed by Bayesian Computation, and Bayesian Methods. The series aims to provide an uptodate overview of the why?, how? and what? of Bayesian statistics. This volume provides a thorough account of key basic concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Informationtheoretic concepts play a central role in the development, which provides, in particular, a detailed treatment of the problem of specification of socalled "prior ignorance". The work is written from the authors' committed Bayesian perspective, but an overview of nonBayesian theories is provided, and each chapter contains a wideranging critical reexamination of controversial issues. The level of mathematics used is such that most material should be accessible to readers with a knowledge of advanced calculus. In particular, no knowledge of abstract measure theory is assumed, and the emphasis throughout is on statistical concepts rather than rigorous mathematics. The book will be an ideal source for all students and researchers in statistics, mathematics, decision analysis, economic and business studies, and all branches of science and engineering, who wish to further their understanding of Bayesian statistics. Contents Preface Chapter 1 Introduction Chapter 2 Foundations Chapter 3 Generalisations Chapter 4 Modelling Chapter 5 Inference Chapter 6 Remodelling Appendix A Summary of Basic Formulae Appendix B NonBayesian Theories References Subject index Author Index
This highly acclaimed text, now available in paperback, provides a thorough account of key concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Informationtheoretic concepts play a central role in the development of the theory, which provides, in particular, a detailed discussion of the problem of specification of socalled ?prior ignorance?. The work is written from the authors?s committed Bayesian perspective, but an overview of nonBayesian theories is also provided, and each chapter contains a wideranging critical reexamination of controversial issues. The level of mathematics used is such that most material is accessible to readers with knowledge of advanced calculus. In particular, no knowledge of abstract measure theory is assumed, and the emphasis throughout is on statistical concepts rather than rigorous mathematics. The book will be an ideal source for all students and researchers in statistics, mathematics, decision analysis, economic and business studies, and all branches of science and engineering, who wish to further their understanding of Bayesian statistics
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