Overview
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. Information-theoretic concepts play a central role in the development of the theory, which provides, in particular, a detailed discussion of the problem of specification of so-called prior ignorance . The work is written from the authors s committed Bayesian perspective, but an overview of non-Bayesian theories is also provided, and each chapter contains a wide-ranging critical re-examination 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
Full Product Details
Author: José M. Bernardo (University de Valencia, Spain) ,
Adrian F. M. Smith (Imperial College of Science, Technology and Medicine, UK)
Publisher: John Wiley & Sons Inc
Imprint: John Wiley & Sons Inc
Dimensions:
Width: 15.40cm
, Height: 3.50cm
, Length: 23.20cm
Weight: 0.936kg
ISBN: 9780471494645
ISBN 10: 047149464
Pages: 608
Publication Date: 28 March 2000
Audience:
College/higher education
,
Professional and scholarly
,
Undergraduate
,
Postgraduate, Research & Scholarly
Format: Paperback
Publisher's Status: Active
Availability: Manufactured on demand
We will order this item for you from a manufactured on demand supplier.
Reviews
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) <br> 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) <br> .,. Bayesians will find it indispensable: non-Bayesians will find, and enjoy, much thought-provoking material to challenge their orthodoxy.... (The Statistician, Vol.51, No.2, 2002)
Author Information
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
