A first course in Bayesian statistical methods / Peter D. Hoff.
By: Hoff, Peter D.Material type: TextSeries: Springer texts in statistics: Publisher: London ; New York : Springer, c2009Description: ix, 270 p. : ill. ; 24 cm.ISBN: 9780387922997 (hbk. : acidfree paper); 0387922997 (hbk.); 9780387924076 (eISBN); 0387924078.Subject(s): Bayesian statistical decision theory | Social sciences -- Statistical methods | Statistique bayésienne | Methode van Bayes | Bayes-VerfahrenDDC classification: 519.5 Online resources: Table of contents | WorldCat details | E-book Fulltext
|Item type||Current location||Collection||Call number||Copy number||Status||Date due||Barcode||Item holds|
|E-Book||EWU Library E-book||Non-fiction||519.5 HOF 2009 (Browse shelf)||Not for loan|
|Text||EWU Library Reserve Section||Non-fiction||519.5 HOF 2009 (Browse shelf)||C-1||Not For Loan||26679|
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|519.5 DEH 2009 A handbook of statistical analyses using SAS /||519.5 DIS 2011 Statistics and scientific method :||519.5 DOI 2008 An introduction to generalized linear models /||519.5 HOF 2009 A first course in Bayesian statistical methods /||519.5 HOI 2005 Introduction to mathematical statistics /||519.5 INT 2017 An introduction to statistical learning :||519.5 JIL 2007 Linear and generalized linear mixed models and their applications /|
Includes bibliographical references (p. -265) and index.
Table of contents 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.