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Bayesian nonparametric statistics = École d'été de probabilités de Saint-Flour LI - 2023 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian nonparametric statistics/ by Ismaël Castillo.
Reminder of title:	École d'été de probabilités de Saint-Flour LI - 2023 /
Author:	Castillo, Ismaël.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xii, 216 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Nonparametric statistics. -
Online resource:	https://doi.org/10.1007/978-3-031-74035-0
ISBN:	9783031740350

Bayesian nonparametric statistics = École d'été de probabilités de Saint-Flour LI - 2023 /
Castillo, Ismaël.

Bayesian nonparametric statisticsÉcole d'été de probabilités de Saint-Flour LI - 2023 /[electronic resource] :by Ismaël Castillo. - Cham :Springer Nature Switzerland :2024. - xii, 216 p. :ill. (some col.), digital ;24 cm. - Lecture notes in mathematics,v. 23581617-9692 ;. - Lecture notes in mathematics ;v. 2358..

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.

ISBN: 9783031740350

Standard No.: 10.1007/978-3-031-74035-0doiSubjects--Topical Terms:

533309
Nonparametric statistics.

LC Class. No.: QA278.8

Dewey Class. No.: 519.542

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300 $a xii, 216 p. : $b ill. (some col.), digital ; $c 24 cm.
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490 1 $a École d'été de probabilités de Saint-Flour
520 $a 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.
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650 2 4 $a Calculus of Variations and Optimization. $3 3538813
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