Subjective and Objective Bayesian Statistics
Principles, Models, and Applications
Résumé
A Classic of Statistical Science, Now Thoroughly Revised and Updated
S. James Press's Bayesian Statistics: Principles, Models, and Applications set the standard for references in Bayesian statistics. It has stood as the classic introduction to the subject for practitioners, researchers, and students alike. Since the publication of the First Edition, the field of Bayesian statistical science has grown so substantially that it has become necessary to rewrite the story. New methodologies have been developed, new techniques have emerged for implementing the Bayesian paradigm, and advances in computer science, numerical analysis, artificial intelligence, and machine learning-including data mining and Bayesian neural networks-have tremendously impacted the field of Bayesian learning. Applications using the Bayesian approach have multiplied as well to span most of the disciplines in the biological, physical, and social sciences.
Subjective and Objective Bayesian Statistics: Principles, Models, and Applications, Second Edition has been rewritten from the bottom up to encompass these changes and to make the text even more useful to the reader. Greatly expanded and revised, this new edition discusses Bayesian theory and principles in depth, expands coverage of many topics to include multivariate procedures, and references applications in many fields to support the usefulness of the subject matter.
Contents
PART I: FOUNDATIONS AND PRINCIPLES.- Background.
- A Bayesian Perspective on Probability.
- The Likelihood Function.
- Bayes Theorem.
- Prior Distributions.
- Markov Chain Monte Carlo Methods (Siddhartha Chib).
- Large Sample Posterior Distributions and Approximations.
- Bayesian Estimation.
- Bayesian Hypothesis Testing.
- Predictivism.
- Bayesian Decision Making.
- Bayesian Inference in the General Linear Model.
- Model Averaging (Merlise Clyde).
- Hierarchical Bayesian Modeling (Alan Zaslavsky).
- Bayesian Factor Analysis.
- Bayesian Inference in Classification and Discrimination.
- 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.
L'auteur - James S. Press
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.
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Wiley |
| Auteur(s) | James S. Press |
| Parution | 08/01/2003 |
| Édition | 2eme édition |
| Nb. de pages | 586 |
| Format | 16 x 24 |
| Couverture | Relié |
| Poids | 945g |
| Intérieur | Noir et Blanc |
| EAN13 | 9780471348436 |
| ISBN13 | 978-0-471-34843-6 |
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