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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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