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Applied Bayesian Modelling (Wiley Series in Probability and Statistics)
 
 
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Applied Bayesian Modelling (Wiley Series in Probability and Statistics) [Hardcover]

Professor Peter Congdon (Author)

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Book Description

0471486957 978-0471486954 May 7, 2003 1
The use of Bayesian statistics has grown significantly in recent years, and will undoubtedly continue to do so. Applied Bayesian Modelling is the follow-up to the author’s best selling book, Bayesian Statistical Modelling, and focuses on the potential applications of Bayesian techniques in a wide range of important topics in the social and health sciences. The applications are illustrated through many real-life examples and software implementation in WINBUGS – a popular software package that offers a simplified and flexible approach to statistical modelling. The book gives detailed explanations for each example – explaining fully the choice of model for each particular problem. The book

· Provides a broad and comprehensive account of applied Bayesian modelling.

· Describes a variety of model assessment methods and the flexibility of Bayesian prior specifications.

· Covers many application areas, including panel data models, structural equation and other multivariate structure models, spatial analysis, survival analysis and epidemiology.

· Provides detailed worked examples in WINBUGS to illustrate the practical application of the techniques described. All WINBUGS programs are available from an ftp site.

The book provides a good introduction to Bayesian modelling and data analysis for a wide range of people involved in applied statistical analysis, including researchers and students from statistics, and the health and social sciences. The wealth of examples makes this book an ideal reference for anyone involved in statistical modelling and analysis.


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Editorial Reviews

Review

"I recommend…highly to statisticians, [and] health researchers...among others to consider keeping on their bookshelf." (Journal of Statistical Computation and Simulation, April 2005)

"…a great book…fills a critical gap in existing literature. It is an excellent book for anyone interested in Bayesian modeling…" (Journal of the American Statistical Association, March 2005)

"It is certainly a fine choice as a supporting reference in either a first or second Bayesian methods course…” (Technometrics, May 2004)

"...has a contemporary feel, with recent developments in financial time series modelling and epidemiology included..." (Short Book Reviews, Vol 23(3), December 2003)

From the Back Cover

The use of Bayesian statistics has grown significantly in recent years, and will undoubtedly continue to do so. Applied Bayesian Modelling is the follow-up to the author’s best selling book, Bayesian Statistical Modelling, and focuses on the potential applications of Bayesian techniques in a wide range of important topics in the social and health sciences. The applications are illustrated through many real-life examples and software implementation in WINBUGS – a popular software package that offers a simplified and flexible approach to statistical modelling. The book gives detailed explanations for each example – explaining fully the choice of model for each particular problem. The book
  • Provides a broad and comprehensive account of applied Bayesian modelling.
  • Describes a variety of model assessment methods and the flexibility of Bayesian prior specifications.
  • Covers many application areas, including panel data models, structural equation and other multivariate structure models, spatial analysis, survival analysis and epidemiology.
  • Provides detailed worked examples in WINBUGS to illustrate the practical application of the techniques described. All WINBUGS programs are available from an ftp site.
The book provides a good introduction to Bayesian modelling and data analysis for a wide range of people involved in applied statistical analysis, including researchers and students from statistics, and the health and social sciences. The wealth of examples makes this book an ideal reference for anyone involved in statistical modelling and analysis.

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Inside This Book (learn more)
First Sentence:
Bayesian analysis of data in the health, social and physical sciences has been greatly facilitated in the last decade by advances in computing power and improved scope for estimation via iterative sampling methods. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
predictive loss criterion, run shows convergence, pseudo marginal likelihood, discrete mixture regressions, missingness model, marginal likelihood identity, scale mixture version, scale mixture approach, first order random walk, marginal likelihood approximations, small domain estimation, minimum posterior predictive loss approach, sampled parameter values, average deviance, inverse likelihoods, unstructured errors, two chain run, autoregressive dependence, posterior averages, ith regression, credible interval, posterior summaries, varying intercepts, varying regression coefficients, latent data
Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Monte Carlo, Oxford University Press, John Wiley, Economic Stat, Clarendon Press, Sociological Methodology, Bayesian Data Analysis, Boca Raton, Average Intercept, Cambridge University Press, Department of Economics, Department of Statistics, Lifetime Data Anal, Marcel Dekker, Regional Sci, Slight Ill, The Statistician, Academic Press, Advanced Structural Equation Modelling, Appendix Table, Applied Stochastic Modelling, Computational Stat, Effects Protestant, Evaluation Rev
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