Bayesian data analysis / Andrew Gelman ... [et al.].
Material type: TextSeries: Texts in statistical science | Texts in statistical sciencePublication details: Boca Raton, Fla. ; London : Chapman & Hall/CRC, 2003.Edition: 2nd edDescription: 640 p. : ill. ; 23 cmISBN:- 9781584883883 (hbk.) :
- 9781584883883
- 519.542 GEL
- QA279.5
Item type | Current library | Call number | Copy number | Status | Date due | Barcode | |
---|---|---|---|---|---|---|---|
Long Loan | TUS: Midlands, Main Library Athlone General Lending | 519.542 GEL (Browse shelf(Opens below)) | 1 | Available | 00211203 |
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519.538 SAG Confidence intervals / | 519.538 SAG Confidence intervals / | 519.54 GOO Resampling methods : a practical guide to data analysis / | 519.542 GEL Bayesian data analysis / | 519.542 SIV Data analysis : a Bayesian tutorial / | 519.544 LEH Theory of point estimation. | 519.55 CHA The analysis of time series : an introduction / |
Previous ed.: London: Chapman & Hall, 1995.
Includes bibliographical references (p. 611-646) and indexes.
Part I: Fundamentals of Bayesian inference -- 1.Background -- 2.Single-parameter models-- 3.Introduction to multiparameter models -- 4.Large-sample inference and frequency properties of Bayesian inferences -- Part II.Fundamentals of Bayesian data analysis -- 5.Herarchical models -- 6.Model checking and improvement -- 7.Modeling accounting for data collection -- 8.Connects and challenges -- 9.General advice -- Part III.Advanced computation -- 10.Overview of computation -- 11.Posterior simulation -- 12.Approximations based on posterior models -- 13.Special topics in computation -- Part IV: Regresson models -- 14.Introduction to regression modles -- 15.Hierarchical linear modles -- 16.Generalized linear models -- 17.Models for robust inference -- Part V.Specific models and problems -- 18.Mixture models -- 19.Multivariate models -- 20.Nonliner models -- 21.Models for missing data -- 22.Decision analysis.
Emphasising practice over theory this second edition incorporates new material on how Bayesian methods are connected to other approaches. It features a stronger focus upon MCMC, more examples and an added chapter on further computation topics.