Bayesian Nonparametrics

J.K. Ghosh, R.V. Ramamoorthi

318 pages, parution le 12/05/2003

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Résumé

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.

Contents

Introduction: Why Bayesian Nonparametrics - An Overview and Summary
Preliminaries and the Finite Dimensional Case
M(X) and Priors on M(X)
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 Right Priors
Exercises
References
Index

L'auteur - J.K. Ghosh

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.

L'auteur - R.V. Ramamoorthi

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.

Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	J.K. Ghosh, R.V. Ramamoorthi
Parution	12/05/2003
Nb. de pages	318
Format	16 x 24
Couverture	Relié
Poids	580g
Intérieur	Noir et Blanc
EAN13	9780387955377
ISBN13	978-0-387-95537-7