S. James Press

Subjective and Objective Bayesian Statistics

Principles, Models, and Applications

• Gebundenes Buch

Produktbeschreibung

This second edition of Bayesian Statisticsexpands and updates the classic text in the field. It features new findings and even more applications to support the usefulness of the material. Neither too technical nor too simplistic, this accessible survey of an important field strikes the perfect balance between theory and application.

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_ Shorter, more concise chapters provide flexible coverage of the subject.
_ Expanded coverage includes: uncertainty and randomness, prior distributions, predictivism, estimation, analysis of variance, and classification and imaging.
_ Includes topics not covered in other books, such as the de Finetti Transform.
_ Author S. James Press is the modern guru of Bayesian statistics.

Produktdetails

• Wiley Series in Probability and Statistics
• Verlag: Wiley & Sons
• 2. Aufl.
• Seitenzahl: 608
• Erscheinungstermin: 9. Dezember 2002
• Englisch
• Abmessung: 240mm x 161mm x 36mm
• Gewicht: 907g
• ISBN-13: 9780471348436
• ISBN-10: 0471348430
• Artikelnr.: 14907546

Autorenporträt

S. JAMES PRESS, PhD, is a Distinguished Professor in the Department of Statistics at the University of California, Riverside. He is the author (with Judith M. Tanur) of The Subjectivity of Scientists and the Bayesian Approach, also published by John Wiley & Sons, Inc., 2001.

Inhaltsangabe

Preface. Preface to the First Edition. A Bayesian Hall of Fame. PART I:
FOUNDATIONS AND PRINCIPLES. 1. Background. 2. A Bayesian Perspective on
Probability. 3. The Likelihood Function. 4. Bayes' Theorem. 5. Prior
Distributions. PART II: NUMERICAL IMPLEMENTATION OF THE BAYESIAN PARADIGM.
6. Markov Chain Monte Carlo Methods (Siddhartha Chib). 7. Large Sample
Posterior Distributions and Approximations. PART III: BAYESIAN STATISTICAL
INFERENCE AND DECISION MAKING. 8. Bayesian Estimation. 9. Bayesian
Hypothesis Testing. 10. Predictivism. 11. Bayesian Decision Making. PART
IV: MODELS AND APPLICATIONS. 12. Bayesian Inference in the General Linear
Model. 13. Model Averaging (Merlise Clyde). 14. Hierarchical Bayesian
Modeling (Alan Zaslavsky). 15. Bayesian Factor Analysis. 16. Bayesian
Inference in Classification and Discrimination. Description of Appendices.
Appendix 1. Bayes, Thomas, (Hilary L. Seal). Appendix 2. Thomas Bayes. A
Bibliographical Note (George A. Barnard). Appendix 3. Communication of
Bayes' Essay to the Philosophical Transactions of the Royal Society of
London (Richard Price). Appendix 4. An Essay Towards Solving a Problem in
the Doctrine of Chances (Reverend Thomas Bayes). Appendix 5. Applications
of Bayesian Statistical Science. Appendix 6. Selecting the Bayesian Hall of
Fame. Appendix 7. Solutions to Selected Exercises. Bibliography. Subject
Index. Author Index.

Rezensionen

"...written in a clear, accessible manner...an enjoyable read and a comprehensive introduction to Bayesian theory and methods." ( Journal of the American Statistical Association , March 2005)
"The book is well written. It should continue the success of the first edition and become the main reference book in the field." ( Interfaces , March-April 2004)

"...among several books I have reviewed, this is one of the best. I very strongly recommend this book to statisticians and applied researchers." ( Journal of Statistical Computation & Simulation , March 2004)

"...a second edition...but really a new book, not merely the first edition with a few changes inserted...a completely restructured book with major new chapters and material..." ( Quarterly of Applied Mathematics , Vol. LXI, No. 2, June 2003)

"...this second edition is a completely restructured book with major new chapters and material..." ( Zentralblatt Math , 2003)