Description 
1 online resource (xiii, 352 pages) : illustrations 
Series 
Springer texts in statistics 

Springer texts in statistics.

Contents 
Statistical Preliminaries  Bayesian Inference and Decision Theory  Utility, Prior and Bayesian Robustness  Large Sample Methods  Choice of Priors for LowDimensional Parameters  Hypothesis Testing and Model Selection  Bayesian Computations  Some Common Problems In Inference  HighDimensional Problems  Some Applications 
Summary 
This is a graduatelevel textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where reallife data can be analyzed using the current techniques of statistical computing. Advances in both lowdimensional and highdimensional problems are covered, as well as important topics such as empirical Bayes and hierarchical Bayes methods and Markov chain Monte Carlo (MCMC) techniques. Many topics are at the cutting edge of statistical research. Solutions to common inference problems appear throughout the text along with discussion of what prior to choose. There is a discussion of elicitation of a subjective prior as well as the motivation, applicability, and limitations of objective priors. By way of important applications the book presents microarrays, nonparametric regression via wavelets as well as DMA mixtures of normals, and spatial analysis with illustrations using simulated and real data. Theoretical topics at the cutting edge include highdimensional model selection and Intrinsic Bayes Factors, which the authors have successfully applied to geological mapping. The style is informal but clear. Asymptotics is used to supplement simulation or understand some aspects of the posterior. J.K. Ghosh has been Director and Jawaharlal Nehru Professor at the Indian Statistical Institute and President of the International Statistical Institute. He is currently a professor of statistics at Purdue University and professor emeritus at the Indian Statistical Institute. He has been the editor of Sankhya and has served on the editorial boards of several journals including the Annals of Statistics. His current interests in Bayesian analysis include asymptotics, nonparametric methods, highdimensional model selection, reliability and survival analysis, bioinformatics, astrostatistics and sparse and not so sparse mixtures. Mohan Delampady and Tapas Samanta are both professors of statistics at the Indian Statistical Institute and both are interested in Bayesian inference, specifically in topics such as model selection, asymptotics, robustness and nonparametrics 
Bibliography 
Includes bibliographical references (pages 317337)and indexes 
Notes 
English 
Subject 
Bayesian statistical decision theory.

Form 
Electronic book

Author 
Delampady, Mohan.


Samanta, Tapas.

LC no. 
2006922766 
ISBN 
0387354336 

0387400842 (hd. bd.) 

9780387354330 

9780387400846 
