Bayesian Nonparametrics - Info and Reading Options
By J.K. Ghosh and R.V. Ramamoorthi
"Bayesian Nonparametrics" is published by Springer in April 8, 2003, it has 304 pages and the language of the book is English.
“Bayesian Nonparametrics” Metadata:
- Title: Bayesian Nonparametrics
- Authors: J.K. GhoshR.V. Ramamoorthi
- Language: English
- Number of Pages: 304
- Publisher: Springer
- Publish Date: April 8, 2003
“Bayesian Nonparametrics” Subjects and Themes:
- Subjects: ➤ Bayesian - Bayesian Nonparametrics - Bayesian statistical decision theory - Nonparametric statistics - Statistics - Mathematical statistics
Edition Identifiers:
- Google Books ID: BnDL64VppHUC
- The Open Library ID: OL7448998M - OL17123919W
- Online Computer Library Center (OCLC) ID: 53251717 - 50155655
- Library of Congress Control Number (LCCN): 2002026665
- ISBN-13: 9780387955377
- ISBN-10: 0387955372
- All ISBNs: 0387955372 - 9780387955377
AI-generated Review of “Bayesian Nonparametrics”:
"Bayesian Nonparametrics" Table Of Contents:
- 1- Introduction
- 2- Preliminaries and the Finite Dimensional Case
- 3- M(X) and Priors on M(X)
- 4- Dirichlet and Polya Tree Process
- 5- Consistency Theorems
- 6- Density Estimation
- 7- Inference for Location Parameter
- 8- Regression Problems
- 9- Uniform Distribution on Infinite Dimensional Spaces
- 10- Survivial Analysis-Dirichlet Priors
- 11- Neutral To Right Priors
Snippets and Summary:
The basic Bayesian model consists of a parameter and a prior distribution for that reflects the investigator's belief regarding .
"Bayesian Nonparametrics" Description:
The Open Library:
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.
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