Bayesian Nonparametrics

Name: Bayesian Nonparametrics
Brand: Springer-Verlag New York Inc.
SKU: 9780387955377
Price: 76345 HUF
Availability: InStock

Szerző: Jayanta K. Ghosh, R. V. Ramarmoorthi

Nyelv:

Kötés: Kemény kötésű

Kiadó: Springer-Verlag New York Inc.

Elérhetőség: Beszállítói készleten

Küldés 10-18 napon belül

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Bayesian nonparametrics has grown tremendously in the last three decades, especially in the last few...

Információk a könyvről

Szerző

Jayanta K. Ghosh, R. V. Ramarmoorthi

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2003

oldal

308

EAN

9780387955377

ISBN

0387955372

Enbook ID

01384451

Kiadó

Springer-Verlag New York Inc.

Súly

1390

Méretek

155 x 235 x 23

Teljes leírás

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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