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A first course in Bayesian statistical methods / Peter D. Hoff.

By: Hoff, Peter DMaterial type: TextTextLanguage: English Series: Springer texts in statisticsPublication details: London ; New York : Springer, c2009. Description: ix, 270 p. : ill. ; 24 cmISBN: 9780387922997 (hbk. : acidfree paper); 0387922997 (hbk.); 9780387924076 (eISBN); 0387924078Subject(s): Bayesian statistical decision theory | Social sciences -- Statistical methods | Statistique bayésienne | Methode van Bayes | Bayes-VerfahrenDDC classification: 519.5 LOC classification: QA279.5 | .H64 2009Online resources: Table of contents | WorldCat details | E-book Fulltext
Contents:
TOC Introduction and examples -- belief, probability and exchangeability -- One-parameter models -- Monte Carlo approximation -- the normal model -- Posterior approximation with the Gibbs sampler -- the multivariate normal model -- Group comparisons and hierarchical modeling -- Linear regression -- Nonconjugate priors and Metropolis-Hastings algorithms -- Linear and generalized linear mixed effects models -- Latent variable methods for ordinal data.
Summary: This compact, self-contained introduction to the theory and application of Bayesian statistical methods is accessible to those with a basic familiarity with probability, yet allows advanced readers to grasp the principles underlying Bayesian theory and method.
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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode Item holds
E-Book E-Book Dr. S. R. Lasker Library, EWU
E-book 		Non-fiction 519.5 HOF 2009 (Browse shelf(Opens below)) Not for loan
Text Text Dr. S. R. Lasker Library, EWU
Reserve Section 		Non-fiction 519.5 HOF 2009 (Browse shelf(Opens below)) C-1 Not For Loan 26679
Total holds: 0

Includes bibliographical references (p. [259]-265) and index.

TOC Introduction and examples -- belief, probability and exchangeability -- One-parameter models -- Monte Carlo approximation -- the normal model -- Posterior approximation with the Gibbs sampler -- the multivariate normal model -- Group comparisons and hierarchical modeling -- Linear regression -- Nonconjugate priors and Metropolis-Hastings algorithms -- Linear and generalized linear mixed effects models -- Latent variable methods for ordinal data.



This compact, self-contained introduction to the theory and application of Bayesian statistical methods is accessible to those with a basic familiarity with probability, yet allows advanced readers to grasp the principles underlying Bayesian theory and method.

AS

Tahur Ahmed

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