Bayesian Nonparametrics

Ghosh, J. K.; Ramarmoorthi, R.V.

ISBN 10: 0387955372 / ISBN 13: 9780387955377

Published by Springer-Verlag New York Inc., 2003

About this Item

Suitable for second year graduate students in statistics. Series: Springer Series in Statistics. Num Pages: 320 pages, biography. BIC Classification: PBT. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 19. Weight in Grams: 1390. . 2003. Hardback. . . . . Books ship from the US and Ireland.

Seller Inventory # V9780387955377

Bibliographic Details

• Title: Bayesian Nonparametrics
• Publisher: Springer-Verlag New York Inc.
• Publication Date: 2003
• Language: English
• ISBN 10: 0387955372
• ISBN 13: 9780387955377
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.

About the Author

R. V. Ramamoorthi is a professor at the Department of Statistics and Probability at Michigan State University.