Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include:
- Stronger focus on MCMC
- Revision of the computational advice in Part III
- New chapters on nonlinear models and decision analysis
- Several additional applied examples from the authors' recent research
- Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more
- Reorganization of chapters 6 and 7 on model checking and data collection
Bayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.
" [...] it is simply the best all-around modern book focused on data analysis currently available. [...] There is enough important additional material here that those with the first edition should seriously consider updating to the new version. [...] when students or colleagues ask me which book they need to start with in order to take them as far as possible down the road toward analyzing their own data, Gelman et al. has been my answer since 1995. The second edition makes this an even more robust choice."
- Lawrence Joseph (Montreal General Hospital and McGill University, Canada), Statistics in Medicine, Vol. 23, 2004
"If you have done some Bayesian modeling, using WinBUGS, and are anxious to take the next steps to more sophisticated modeling and diagnostics, then the book offers a wealth of advice [...] This is a book that challenges the user in its sophisticated approach toward data analysis in general and Bayesian methods in particular. I am thoroughly excited to have this book in hand to supplement course material and to offer research collaborators and clients at our consulting lab more sophisticated methods to solve their research problems."
- John Grego, University of South Carolina, USA
"Bayesian Data Analysis is easily the most comprehensive, scholarly, and thoughtful book on the subject, and I think will do much to promote the use of Bayesian methods"
- David Blackwell, Department of Statistics, University of California, Berkeley, USA
Praise for the first edition:
"A tour de force [...] it is far more than an introductory text, and could act as a companion for a working scientist from undergraduate level through to professional life."
- Robert Matthews (Aston University), New Scientist
"an essential reference text for any applied statistician"
- Stephen Brooks (University of Cambridge), The Statistician
"will contribute to closing the gap between scientists and statisticians"
- Sander Greenland (University of California, Los Angeles), American Journal of Epidemiology
"an excellent teaching reference for advanced undergraduate and graduate courses"
- Nicky Best, (Imperial College School of Medicine), Statistics in Medicine
FUNDAMENTALS OF BAYESIAN INFERENCE
Introduction to Multiparameter Models
Large-Sample Inference and Connections to Standard Statistical Methods
FUNDAMENTALS OF BAYESIAN DATA ANALYSIS
Model Checking and Improvement
Modeling Accounting for Data Collection
Connections and Controversies
Overview of Computation
Approximations Based on Posterior Modes
Topics in Computation
Introduction to Regression Models
Hierarchical Linear Models
Generalized Linear Models
Models for Robust Inference and Sensitivity Analysis
Analysis of Variance
SPECIFIC MODELS AND PROBLEMS
Models for Missing Data
A: Standard Probability Distributions
B: Outline of Proofs of Asymptotic Theorems
C: Example of Computation in R and Bugs
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