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Field name	Details
Dewey Class	519.5
Title	Bayesian Nonparametrics ([EBook] /) / by J. K. Ghosh, R. V. Ramamoorthi.
Author	Ghosh, J. K.
Added Personal Name	Ramamoorthi, R. V. author.
Other name(s)	SpringerLink (Online service)
Publication	New York, NY : : Springer New York, , 2003.
Physical Details	XII, 308 p. : online resource.
Series	Springer Series in Statistics 0172-7397
ISBN	9780387226545
Summary Note	Bayesian nonparametrics has grown tremendously in the last three decades, especially in the last few years. This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. While the book is of special interest to Bayesians, it will also appeal to statisticians in general because Bayesian nonparametrics offers a whole continuous spectrum of robust alternatives to purely parametric and purely nonparametric methods of classical statistics. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian nonparametrics. Though the emphasis of the book is on nonparametrics, there is a substantial chapter on asymptotics of classical Bayesian parametric models. Jayanta Ghosh has been Director and Jawaharlal Nehru Professor at the Indian Statistical Institute and President of the International Statistical Institute. He is currently professor of statistics at Purdue University. He has been editor of Sankhya and served on the editorial boards of several journals including the Annals of Statistics. Apart from Bayesian analysis, his interests include asymptotics, stochastic modeling, high dimensional model selection, reliability and survival analysis and bioinformatics. R.V. Ramamoorthi is professor at the Department of Statistics and Probability at Michigan State University. He has published papers in the areas of sufficiency invariance, comparison of experiments, nonparametric survival analysis and Bayesian analysis. In addition to Bayesian nonparametrics, he is currently interested in Bayesian networks and graphical models. He is on the editorial board of Sankhya.:
Contents note	Introduction: Why Bayesian Nonparametrics—An Overview and Summary -- Preliminaries and the Finite Dimensional Case -- M(?) and Priors on M(?) -- Dirichlet and Polya tree process -- Consistency Theorems -- Density Estimation -- Inference for Location Parameter -- Regression Problems -- Uniform Distribution on Infinite-Dimensional Spaces -- Survival Analysis—Dirichlet Priors -- Neutral to the Right Priors -- Exercises.
System details note	Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users)
Internet Site	http://dx.doi.org/10.1007/b97842
Links to Related Works	Subject References: Statistical Theory and Methods . Statistics . Authors: Ghosh, J. K. . Ramamoorthi, R. V. . Corporate Authors: SpringerLink (Online service) . Series: Springer Series in Statistics . Classification: 519.5 .

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