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Loading... Bayesian Data Analysisby Andrew Gelman, John B. Carlin, Donald B. Rubin, Hal S. Stern
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Sign up for LibraryThing to find out whether you'll like this book. No current Talk conversations about this book. NA In the end I'm a bit torn. Everybody describes it as the bible of bayesian statistics and it indeed covers a wide range of topics and by that supersedes all other general textbooks on bayesianism I know of. A great amount of literature is given after each chapter for everyone who wants to learn more about specific stuff. As a first time introduction on the other hand it's an incredible bad choice - if you don't know the basics you are going to have a bad time. no reviews | add a review
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"Preface This book is intended to have three roles and to serve three associated audiences: an introductory text on Bayesian inference starting from first principles, a graduate text on effective current approaches to Bayesian modeling and computation in statistics and related fields, and a handbook of Bayesian methods in applied statistics for general users of and researchers in applied statistics. Although introductory in its early sections, the book is definitely not elementary in the sense of a first text in statistics. The mathematics used in our book is basic probability and statistics, elementary calculus, and linear algebra. A review of probability notation is given in Chapter 1 along with a more detailed list of topics assumed to have been studied. The practical orientation of the book means that the reader's previous experience in probability, statistics, and linear algebra should ideally have included strong computational components. To write an introductory text alone would leave many readers with only a taste of the conceptual elements but no guidance for venturing into genuine practical applications, beyond those where Bayesian methods agree essentially with standard non-Bayesian analyses. On the other hand, we feel it would be a mistake to present the advanced methods without first introducing the basic concepts from our data-analytic perspective. Furthermore, due to the nature of applied statistics, a text on current Bayesian methodology would be incomplete without a variety of worked examples drawn from real applications. To avoid cluttering the main narrative, there are bibliographic notes at the end of each chapter and references at the end of the book"-- No library descriptions found. |
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Google Books — Loading... GenresMelvil Decimal System (DDC)519.542Natural sciences and mathematics Mathematics Applied Mathematics, Probabilities Statistical MathematicsLC ClassificationRatingAverage:
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