gogo
Amazon cover image
Image from Amazon.com

Bayesian data analysis / Andrew Gelman ... [et al.].

Contributor(s): Material type: TextTextSeries: 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
Subject(s): DDC classification:
  • 519.542 GEL
LOC classification:
  • QA279.5
Contents:
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.
Summary: 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.
Holdings
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

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.

Powered by Koha