Bayesian Statistics and Its Applications
Satyanshu K. Upadhyay, Umesh Singh, Dipak Dey
Anshan, 2007 - Mathematics - 507 pages
In the last two decades, Bayesian Statistics has acquired immense importance and has penetrated almost every area including those where the application of statistics appeared to be a remote possibility. This volume provides both theoretical and practical insights into the subject with detailed up-to-date material on various aspects. It serves two important objectives - to offer a thorough background material for theoreticians and gives a variety of applications for applied statisticians and practitioners. Consisting of 33 chapters, it covers topics on biostatistics, econometrics, reliability, image analysis, Bayesian computation, neural networks, prior elicitation, objective Bayesian methodologies, role of randomisation in Bayesian analysis, spatial data analysis, nonparametrics and a lot more. The book will serve as an excellent reference work for updating knowledge and for developing new methodologies in a wide variety of areas. It will become an invaluable tool for statisticians and the practitioners of Bayesian paradigm.
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Prior Model for Reconstruction of Contingency Table
Why Bayesianism? A Primer on a Probabilistic Philosophy of Science
On Coregionalized Models for Spatially Replicated Experiments
22 other sections not shown
algorithm American Statistical Association applications assessment assume baseline Bayes factor Bayesian analysis Bayesian approach Bayesian inference Bayesian methods Bayesian Statistics Berger BMOM CALGB coefficients components computed conditional distribution consider covariates credible intervals data set defined denote depend Dirichlet process discussion elicitation entropy error estimates evidence example finite population frequentist full conditional Gaussian Gibbs sampler given hierarchical model images independent Interactive polynomial interval intrinsic iterations Journal likelihood function Markov chain matrix MCMC measure mixture models mixture of normals Monte Carlo multivariate nonparametric normal distribution observed obtained p-value parameters plots Polya tree posterior density posterior distribution posterior mean prior distribution prior predictive prior-data conflict probability problem procedure proposed random effects regime regression sample Section selection semiparametric Separable polynomial simulation spatial specification standard deviation survival Table Theorem theory threshold values variance vector Zellner zero