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first volume of a related series of three—

By R. B. Kellogg, Bayesian Theory and M. Bernardo


Bayesian Theory, Bayesian Computation, and Bayesian Methods—which set out to give an up-to-date overview of our version of the why? how? and what? of Bayesian Statistics. ” The 11-page first chapter of the book presents a clear, useful overview of the balance. I am sure that this book will be reviewed in the mainstream statistics literature often and deeply. Hence, rather than writing a review intended for statisticians, I have tried to prepare a review I hope speaks to a broad-based scientific community. Most statistics researchers were Bayesian in their outlook until the early portion of this century, when Fisher, Neyman, and others invented what is oddly now known as “classical statistics. ” Though the tradition of Bayes and Laplace was maintained by some (de Finetti, Jeffreys, Savage, Lindley, and others), classical statistics became dominant in practice as well as a fertile target for an emerging mathematical statistics community. Classical statistics finds its footing in the development of procedures that are assessed based on their long-run behavior in repeated use in an (imaginary) infinite sequence of identical repetitions of an experiment. Such approaches are known as frequency based, by analogy with the frequency definition of probability. Bayesian statistics has a rich collection of bases for its development. First, Bayesian analysis treats uncertain quantities as if they were random. With this focus, learn-Publishers are invited to send books fo

Year: 2011
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