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

Contributor(s): Material type: TextTextSeries: Texts in statistical science | Texts in statistical sciencePublication details: London : Chapman & Hall, 1995. Description: xix, 526 pages : illustrations 24 cmISBN:
  • 0412039915
Subject(s):
Contents:
Part I. Fundamentals of Bayesian Inference -- 1. Background -- 2. Single-parameter models -- 3. Introduction to multiparameter models -- 4. Large-sample inference and connections to standard statistical methods -- Part II. Fundamentals of Bayesian Data Analysis -- 5. Hierarchical models -- 6. Model checking and sensitivity analysis -- 7. Study design in Bayesian analysis -- 8. Introduction to regression models -- Part III. Advanced computation -- 9. Approximations based on posterior modes -- 10. Posterior simulation and integration -- 11. Markov chain simulation -- Part IV. Specific models -- 12. Models for robust inference and sensitivity analysis -- 13. Hierarchical linear models -- 14. Generalized linear models -- 15. Multivariate models -- 16. Mixture models -- 17. Models for missing data -- 18. Concluding advice.
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Item type Current library Call number Copy number Status Notes Date due Barcode
Book Book ENSIGN LIBRARY General Stacks QA279.5 B34 (Browse shelf(Opens below)) C.1 Available ekp 15/12/22

Reprint 1997.

Includes bibliographical references (pages 489-512) and indexes.

Part I. Fundamentals of Bayesian Inference -- 1. Background -- 2. Single-parameter models -- 3. Introduction to multiparameter models -- 4. Large-sample inference and connections to standard statistical methods -- Part II. Fundamentals of Bayesian Data Analysis -- 5. Hierarchical models -- 6. Model checking and sensitivity analysis -- 7. Study design in Bayesian analysis -- 8. Introduction to regression models -- Part III. Advanced computation -- 9. Approximations based on posterior modes -- 10. Posterior simulation and integration -- 11. Markov chain simulation -- Part IV. Specific models -- 12. Models for robust inference and sensitivity analysis -- 13. Hierarchical linear models -- 14. Generalized linear models -- 15. Multivariate models -- 16. Mixture models -- 17. Models for missing data -- 18. Concluding advice.

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