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عنوان
Fundamentals of nonparametric Bayesian inference /

پدید آورنده
Subhashis Ghosal, North Carolina State University ; Aad van der Vaart, Leiden University.

موضوع
Bayesian statistical decision theory.,Nonparametric statistics.,Bayesian statistical decision theory.,Nonparametric statistics.

رده
QA278
.
8
.
G46
2017

کتابخانه
Center and Library of Islamic Studies in European Languages

محل استقرار
استان: Qom ـ شهر: Qom

Center and Library of Islamic Studies in European Languages

تماس با کتابخانه : 32910706-025

INTERNATIONAL STANDARD BOOK NUMBER

(Number (ISBN
0521878268
(Number (ISBN
9780521878265
Erroneous ISBN
9781139029834 (ebk)

TITLE AND STATEMENT OF RESPONSIBILITY

Title Proper
Fundamentals of nonparametric Bayesian inference /
General Material Designation
[Book]
First Statement of Responsibility
Subhashis Ghosal, North Carolina State University ; Aad van der Vaart, Leiden University.

.PUBLICATION, DISTRIBUTION, ETC

Place of Publication, Distribution, etc.
New York, NY :
Name of Publisher, Distributor, etc.
Cambridge University Press,
Date of Publication, Distribution, etc.
2017.
Date of Publication, Distribution, etc.
©2017

PHYSICAL DESCRIPTION

Specific Material Designation and Extent of Item
xxiv, 646 pages ;
Dimensions
27 cm.

SERIES

Series Title
Cambridge series in statistical and probabilistic mathematics ;
Volume Designation
44

INTERNAL BIBLIOGRAPHIES/INDEXES NOTE

Text of Note
Includes bibliographical references and indexes.

CONTENTS NOTE

Text of Note
Introduction -- Priors on function spaces -- Prior on spaces of probability measures -- Dirichlet processes -- Dirichlet process mixtures -- Consistency : general theory -- Consistency : examples -- Contraction rates : general theory -- Contraction rates : examples -- Adaptation and model selection -- Gaussian process priors -- Infinite-dimensional Bernstein-von Mises theorem -- Survival analysis -- Discrete random structures.
0

SUMMARY OR ABSTRACT

Text of Note
Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical advances of the past twenty years to synthesize all aspects of Bayesian nonparametrics, from prior construction to computation and large sample behavior of posteriors. Because understanding the behavior of posteriors is critical to selecting priors that work, the large sample theory is developed systematically, illustrated by various examples of model and prior combinations. Precise sufficient conditions are given, with complete proofs, that ensure desirable posterior properties and behavior. Each chapter ends with historical notes and numerous exercises to deepen and consolidate the reader's understanding, making the book valuable for both graduate students and researchers in statistics and machine learning, as well as in application areas such as econometrics and biostatistics. --

TOPICAL NAME USED AS SUBJECT

Bayesian statistical decision theory.
Nonparametric statistics.
Bayesian statistical decision theory.
Nonparametric statistics.

DEWEY DECIMAL CLASSIFICATION

Number
519
.
5/42
Edition
23

LIBRARY OF CONGRESS CLASSIFICATION

Class number
QA278
.
8
Book number
.
G46
2017

PERSONAL NAME - PRIMARY RESPONSIBILITY

Ghosal, Subhashis

PERSONAL NAME - ALTERNATIVE RESPONSIBILITY

Vaart, A. W. van der

ORIGINATING SOURCE

Date of Transaction
20200822120433.0
Cataloguing Rules (Descriptive Conventions))
rda

ELECTRONIC LOCATION AND ACCESS

Electronic name
 مطالعه متن کتاب 

[Book]

Y

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