Bayesian Data Analysis (Texts in Statistical Science Series) / Edition 2

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Overview

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.

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Product Details

Table of Contents

FUNDAMENTALS OF BAYESIAN INFERENCE
Background
Single-Parameter Models
Introduction to Multiparameter Models
Large-Sample Inference and Connections to Standard Statistical Methods

FUNDAMENTALS OF BAYESIAN DATA ANALYSIS
Hierarchical Models
Model Checking and Improvement
Modeling Accounting for Data Collection
Connections and Controversies
General Advice

ADVANCED COMPUTATION
Overview of Computation
Posterior Simulation
Approximations Based on Posterior Modes
Topics in Computation

REGRESSION MODELS
Introduction to Regression Models
Hierarchical Linear Models
Generalized Linear Models
Models for Robust Inference and Sensitivity Analysis
Analysis of Variance

SPECIFIC MODELS AND PROBLEMS
Mixture Models
Multivariate Models
Nonlinear Models
Models for Missing Data
Decision Analysis

APPENDICES
A: Standard Probability Distributions
B: Outline of Proofs of Asymptotic Theorems
C: Example of Computation in R and Bugs
References

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  • Anonymous

    Posted November 21, 2005

    Very Vague

    I did not care for this text. It is very chatty making it unclear as to what is actually going on mathematically and computationally. It is not that the text is unreadable or poorly written, but you will not read the text and then be able to solve math or data analysis problems. The authors talk about much, but may actually never formally set up a problem. If you are not already proficient in Bayesian statistics and linear models, you will have a hard time getting anything beyond broad ideas.

    1 out of 1 people found this review helpful.

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  • Anonymous

    Posted June 29, 2010

    No text was provided for this review.

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