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... PANJER, and WILLMUIa#39; ~ Solutions Manual to Accompany Loss Models: From Data to Decisions KOVALENKO, KUZNEa#39;IZOV. and PEGG ... LAD - Operational Subjective Statistical Methods: A Mathematical, Philosophical, and Historical Introduction LANGE, RYAN, BILLARD. ... Decision Processes: Discrete Stochastic Dynamic Programming RACHEV a#39; Probability Metrics and the Stability of Stochasticanbsp;...
|Author||:||José M. Bernardo, Adrian F. M. Smith|
|Publisher||:||John Wiley & Sons - 2009-09-25|