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Bayesian Nonparametric Statistics
Ismaël Castillo
Livre broché | Anglais | Lecture Notes in Mathematics | École d'Été de Probabilités de Saint-Flour | n° 2358
63,45 €
+ 126 points
Description
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.
Spécifications
Parties prenantes
- Auteur(s) :
- Editeur:
Contenu
- Nombre de pages :
- 216
- Langue:
- Anglais
- Collection :
- Tome:
- n° 2358
Caractéristiques
- EAN:
- 9783031740343
- Date de parution :
- 26-12-24
- Format:
- Livre broché
- Format numérique:
- Trade paperback (VS)
- Dimensions :
- 155 mm x 235 mm
