Essential Bayesian Models

Name: Essential Bayesian Models
Price: 27.74 USD
Availability: InStock
ISBN: 9780444537324

ISBN-10: 0444537325

ISBN-13: 9780444537324

Edition: 2010

Authors: C. R. Rao, D. K. Dey

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Book details

List price: $85.95
Copyright year: 2010
Publisher: Elsevier Science & Technology
Publication date: 11/17/2010
Binding: Hardcover
Pages: 586
Size: 6.50" wide x 9.00" long x 0.75" tall
Weight: 2.332
Language: English

C. R. Rao, born in India, is one of this century's foremost statisticians, and received his education in statistics at the Indian Statistical Institute (ISI), Calcutta. He is Emeritus Holder of the Eberly Family Chair in Statistics at Penn State and Director of the Center for Multivariate Analysis. He has long been recognized as one of the world's top statisticians, and has been awarded 34 honorary doctorates from universities in 19 countries spanning 6 continents. His research has influenced not only statistics, but also the physical, social and natural sciences and engineering. In 2011 he was recipient of the Royal Statistical Society's Guy Medal in Gold which is awarded triennially to…



List of Contributors



Bayesian Inference for Causal Effects




Causal inference primitives



A brief history of the potential outcomes framework



Models for the underlying data - Bayesian inference



Complications



Model Selection and Hypothesis Testing based on Objective Probabilities and Bayes Factors




Introduction



Objective Bayesian model selection methods



More general training samples



Prior probabilities



Conclusions


Acknowledgements



Bayesian Model Checking and Model Diagnostics




Introduction



Model checking overview



Approaches for checking if the model is consistent with the data



Posterior predictive model checking techniques



Application 1



Application 2



Conclusions



Bayesian Nonparametric Modeling and Data Analysis: An Introduction




Introduction to Bayesian nonparametrics



Probability measures on spaces of probability measures



Illustrations



Concluding remarks



Some Bayesian Nonparametric Models




Introduction



Random distribution functions



Mixtures of Dirichlet processes



Random variate generation for NTR processes



Sub-classes of random distribution functions



Hazard rate processes



Polya trees



Beyond NTR processes and Polya trees



Bayesian Modeling in the Wavelet Domain




Introduction



Bayes and wavelets



Other problems


Acknowledgements



Bayesian Methods for Function Estimation




Introduction



Priors on infinite-dimensional spaces



Consistency and rates of convergence



Estimation of cumulative probability distribution



Density estimation



Regression function estimation



Spectral density estimation



Estimation of transition density



Concluding remarks



MCMC Methods to Estimate Bayesian Parametric Models




Motivation



Bayesian ingredients



Bayesian recipe



How can the Bayesian pie burn



MCMC methods



The perfect Bayesian pie: How to avoid "burn-in" issues



Conclusions



Bayesian Computation: From Posterior Densities to Bayes Factors, Marginal Likelihoods, and Posterior Model Probabilities




Introduction



Posterior density estimation



Marginal posterior densities for generalized linear models



Savage-Dickey density ratio



Computing marginal likelihoods



Computing posterior model probabilities via informative priors



Concluding remarks



Bayesian Modelling and Inference on Mixtures of Distributions




Introduction



The finite mixture framework



The mixture conundrum



Inference for mixture models with known number of components



Inference for mixture models with unknown number of components



Extensions to the mixture framework


Acknowledgements



Variable Selection and Covariance Selection in Multivariate Regression Models




Introduction



Model description



Sampling scheme



Real data



Simulation study



Summary



Dynamic Models




Model structure, inference and practical aspects



Markov Chain Monte Carlo



Sequential Monte Carlo



Extensions


Acknowledgements



Elliptical Measurement Error Models - A Bayesian Approach




Introduction



Elliptical measurement error models



Diffuse prior distribution for the incidental parameters



Dependent elliptical MEM



Independent elliptical MEM



Application


Acknowledgements



Bayesian Sensitivity Analysis in Skew-Elliptical Models




Introduction



Definitions and properties of skew-elliptical distributions



Testing of asymmetry in linear regression model



Simulation results



Conclusions


Acknowledgements



Bayesian Methods for DNA Microarray Data Analysis




Introduction



Review of microarray technology



Statistical analysis of microarray data



Bayesian models for gene selection



Differential gene expression analysis



Bayesian clustering methods



Regression for grossly overparametrized models



Concluding remarks


Acknowledgements



Bayesian Biostatistics




Introduction



Correlated and longitudinal data



Time to event data



Nonlinear modeling



Model averaging



Bioinformatics



Discussion



Innovative Bayesian Methods for Biostatistics and Epidemiology




Introduction



Meta-analysis and multicentre studies



Spatial analysis for environmental epidemiology



Adjusting for mismeasured variables



Adjusting for missing data



Sensitivity analysis for unobserved confounding



Ecological inference



Bayesian model averaging



Survival analysis



Case-control analysis



Bayesian applications in health economics



Discussion



Modeling and Analysis for Categorical Response Data




Introduction



Binary responses



Ordinal response data



Sequential ordinal model



Multivariate responses



Longitudinal binary responses



Longitudinal multivariate responses



Conclusion



Bayesian Methods and Simulation-Based Computation for Contingency Tables




Motivation for Bayesian methods



Advances in simulation-based Bayesian calculation



Early Bayesian analyses of categorical data



Bayesian smoothing of contingency tables



Bayesian interaction analysis



Bayesian tests of equiprobability and independence



Bayes factors for GLM's with application to log-linear models



Use of BIC in sociological applications



Bayesian model search for loglinear models



The future



Teaching Bayesian Thought to Nonstatisticians




Introduction



A brief literature review



Commonalities across groups in teaching Bayesian methods



Motivation and conceptual explanations: One solution



Conceptual mapping



Active learning and repetition



Assessment



Conclusions


Subject Index