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Bayesian nonparametric statistics (Record no. 9643)

MARC details
000 -LEADER
fixed length control field	02271nam a22002177a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field	20250510171159.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	250507b \|\|\|\|\|\|\|\| \|\|\|\| 00\| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number	9783031740343
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number	519.542
Item number	CAS
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name	Castillo, Ismaël
245 ## - TITLE STATEMENT
Title	Bayesian nonparametric statistics
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc.	Springer
Place of publication, distribution, etc.	Cham
Date of publication, distribution, etc.	2024
300 ## - PHYSICAL DESCRIPTION
Extent	xii, 214 p.
365 ## - TRADE PRICE
Price type code	EURO
Price amount	59.99
490 ## - SERIES STATEMENT
Series statement	Lecture Notes in Mathematics (Springer-Verlag), 2358
500 ## - GENERAL NOTE
General note	Table of contents:<br/>Introduction, Rates I<br/>Ismaël Castillo<br/>Pages 1-24<br/>Rates II and First Examples<br/>Ismaël Castillo<br/>Pages 25-46<br/>Adaptation I: Smoothness<br/>Ismaël Castillo<br/>Pages 47-65<br/>Adaptation II: High-Dimensions and Deep Neural Networks<br/>Ismaël Castillo<br/>Pages 67-102<br/>Bernstein-von Mises I: Functionals<br/>Ismaël Castillo<br/>Pages 103-123<br/>Bernstein-von Mises II: Multiscale and Applications<br/>Ismaël Castillo<br/>Pages 125-150<br/>Classification and Multiple Testing<br/>Ismaël Castillo<br/>Pages 151-172<br/>Variational Approximations<br/>Ismaël Castillo<br/>Pages 173-188<br/>[https://link.springer.com/book/10.1007/978-3-031-74035-0]
520 ## - SUMMARY, ETC.
Summary, etc.	This up-to-date overview of Bayesian nonparametric statistics provides both an introduction to the field and coverage of recent research topics, including deep neural networks, high-dimensional models and multiple testing, Bernstein-von Mises theorems and variational Bayes approximations, many of which have previously only been accessible through research articles. Although Bayesian posterior distributions are widely applied in astrophysics, inverse problems, genomics, machine learning and elsewhere, their theory is still only partially understood, especially in complex settings such as nonparametric or semiparametric models. Here, the available theory on the frequentist analysis of posterior distributions is outlined in terms of convergence rates, limiting shape results and uncertainty quantification. Based on lecture notes for a course given at the St-Flour summer school in 2023, the book is aimed at researchers and graduate students in statistics and probability.<br/><br/>(https://link.springer.com/book/10.1007/978-3-031-74035-0)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element	Nonparametric statistics
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element	Bayesian statistical--Decision theory
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type	Book
Source of classification or shelving scheme	Dewey Decimal Classification

Holdings
Withdrawn status	Lost status	Source of classification or shelving scheme	Damaged status	Not for loan	Collection code	Bill No	Bill Date	Home library	Current library	Shelving location	Date acquired	Source of acquisition	Cost, normal purchase price	Total Checkouts	Full call number	Accession Number	Date last seen	Copy number	Cost, replacement price	Price effective from	Koha item type
		Dewey Decimal Classification			Operations Management & Quantitative Techniques	TB4762	21-03-2025	Indian Institute of Management LRC	Indian Institute of Management LRC	General Stacks	03/28/2025	Technical Bureau India Pvt. Ltd.	3634.20		519.542 CAS	008585	03/28/2025	1	5591.07	03/28/2025	Book