Bayesian Modeling Using WinBUGS

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John Wiley & Sons, Sep 20, 2011 - Mathematics - 520 pages
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A hands-on introduction to the principles of Bayesian modelingusing WinBUGS

Bayesian Modeling Using WinBUGS provides an easilyaccessible introduction to the use of WinBUGS programmingtechniques in a variety of Bayesian modeling settings. The authorprovides an accessible treatment of the topic, offering readers asmooth introduction to the principles of Bayesian modeling withdetailed guidance on the practical implementation of keyprinciples.

The book begins with a basic introduction to Bayesian inferenceand the WinBUGS software and goes on to cover key topics,including:

  • Markov Chain Monte Carlo algorithms in Bayesian inference

  • Generalized linear models

  • Bayesian hierarchical models

  • Predictive distribution and model checking

  • Bayesian model and variable evaluation

Computational notes and screen captures illustrate the use ofboth WinBUGS as well as R software to apply the discussedtechniques. Exercises at the end of each chapter allow readers totest their understanding of the presented concepts and all datasets and code are available on the book's related Web site.

Requiring only a working knowledge of probability theory andstatistics, Bayesian Modeling Using WinBUGS serves as anexcellent book for courses on Bayesian statistics at theupper-undergraduate and graduate levels. It is also a valuablereference for researchers and practitioners in the fields ofstatistics, actuarial science, medicine, and the social scienceswho use WinBUGS in their everyday work.

 

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Bayesian modeling using WinBUGS

User Review  - patrickaz - Overstock.com

Great book for anyone looking to implement Bayesian modeling using the WinBUGS tool. Lots of examples for the specific tool being used are always very helpful. Read full review

Contents

MARKOV CHAINMONTE CARLO ALGORITHMS
2-2
MISSING OBSERVATIONS USING MCMC
2-10
WinBUGS SOFTWARE INTRODUCTION SETUP
2-23
WinBUGSSOFTWARE ILLUSTRATION RESULTSAND
15
INTRODUCTION TO BAYESIAN MODELS NORMAL
42
SIMPLE MODEL 4 2 FURTHER OUTPUT ANALYSIS USING THE INFERENCE MENU 4 3 MULTIPLE CHAINS
6-4
INCORPORATING CATEGORICAL VARIABLES IN NORMAL MODELSAND FURTHER MODELING ISSUES
6-6
BAYESIAN MODELAND VARIABLE EVALUATION
6-11
MODELSFORPOSITIVE CONTINUOUS DATA COUNT
6-69
BAYESIAN HIERARCHICAL MODELS
10
THE PREDICTIVE DISTRIBUTION AND MODEL
9-24
MODEL SPECIFICATION VIA DIRECTED ACYCLIC
10-77
CHECKING CONVERGENCE USING CODABOA
10-83
NOTATION SUMMARY
10-95
INDEX
10-111
Copyright

INTRODUCTION TOGENERALIZED LINEAR MODELS
6-32

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About the author (2011)

Ioannis Ntzoufras, PhD, is Assistant Professor of Statistics at Athens University of Economics and Business (Greece). Dr. Ntzoufras has published numerous journal articles in his areas of research interest, which include Bayesian statistics, statistical analysis and programming, and generalized linear models.

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