5/01/2012

Handbook of Statistics, Volume 25: Bayesian Thinking, Modeling and Computation Review

Handbook of Statistics, Volume 25: Bayesian Thinking, Modeling and Computation
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Professor Krishnaiah had the idea of creating a series of handbooks on statstics with each volume covering a major topic or branch of statistical methodology. The handbooks are intended to have research articles that may contain very new developments in the field but also include articles that survey the field to give the professional statistician a great reference to look at when wishing to apply a technique or do new research in the area.
The famous Professor C. R. Rao took over the editing job after Krishnaiah's passing. Professor Rao had previously coedited some of these volumes with Krishnaiah and contributed articles. It was Krishnaiah who brought Rao to the United States and the University of Pittsburgh from India.
This volume covers Bayesian statistics with expository articles on Bayesian principles, statistical models and the computational aspects of the Markov Chain Monte Carlo algorithms that now allow for the solution of many Bayesian problems that were previously not tractable.

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This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to summarize inferences using simulation. The book covers wide range of topics including objective and subjective Bayesian inferences with a variety of applications in modelling categorical, survival, spatial, spatiotemporal, Epidemiological, software reliability, small area and micro array data. The book concludes with a chapter on how to teach Bayesian thoughts to nonstatisticians.
Key Features:
-Critical thinking on causal effects -Objective Bayesian philosophy -Nonparametric Bayesian methodology -Simulation based computing techniques -Bioinformatics and Biostatistics Key Features:
Critical thinking on causal effects Objective Bayesian philosophy Nonparametric Bayesian methodology Simulation based computing techniques Bioinformatics and Biostatistics

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