Reviews of first edition:
'A clearly written and comprehensive account...an excellent
book in an excellent series.' Mathematics Today
‘This very well-written book has been designed to
complement the Kendall’s series by presenting therein
the Bayesian point of view. … The author has skilfully
managed to cover a great deal of ground in this volume and
readers will find few topics of interest to be missing.’
Short Book Reviews
Key Features:
- Clearly written with a comprehensive coverage of the theory
and methodology underlying all Bayesian methods
- The most up-to-date account
- Includes chapters on robustness, computation and MCMC
methods
- Exercises supplied at the end of each chapter.
Description:
The Bayesian approach to statistics is now widely accepted
as theoretically sound and practically viable. Enormous advances
in Bayesian methodology in recent years have resulted in a
great expansion of applications of Bayesian statistics in
a wide variety of fields. This second edition is a response
to the developments and advances that have taken place in
this area over the last few years and offers the reader an
up-to-date and comprehensive overview of Bayesian statistics.
The new edition of Bayesian Inference has been expanded to
include new chapters on Markov chain Monte Carlo methods,
discrete data models and non-parametric models. Existing chapters
have also been thoroughly revised and updated and there is
greater coverage of computational methods and of model comparison
and criticism. There is also a new chapter of case studies,
providing practical illustrations of the theory presented
throughout the book.
Like the other volumes in the Kendall’s Library of
Statistics, the first edition of Bayesian Inference provided
a good selection of exercises at the end of each chapter.
This popular feature is retained in the new edition, with
many new exercises to deepen the reader’s understanding.
Readership:
All statisticians and anyone needing to know more about
Bayesian statistics.
Contents:
Preface
Glossary of Abbreviations
1. The Bayesian method
2. Inference and decisions
3. General principles and theory
4. Subjective probability
5. Non-subjective theories
6. Prior distributions
7. Model comparison
8. Robustness and model criticism
9. Computation
10. Markov Chain Monte Carlo
11. The linear model
12. Discrete data models
13. Nonparametric models
14. Other standard models
15. Short case studies
Bibliography
Author index
Subject index
* Please note, you are welcome to print the sample chapter
files for personal use but all material included in these
files is copyrighted to Hodder Arnold and is not for further
distribution without the permission of the publishers. |
