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vls/2603nrwcece.visuallibrary.netvdBayesian nonparametric statisticsÉcole d'Été de Probabilités de Saint-Flour LI - 2023Castillo, IsmaëlVerfasserauttextTextKonferenzschrift-2023-Saint-Flour-l’ÉtangbookszXA-CHCham, SwitzerlandSpringer[2024]© 20242024monographicengprintxii, 214 Seiten Illustrationenohne Hilfsmittel zu benutzenBandThis 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.Ismaël CastilloMachine learningMaschinelles LernenOptimierungOptimizationProbability & statisticsStatistical physicsStatistische PhysikStochasticsStochastikWahrscheinlichkeitsrechnung und StatistikNichtparametrische StatistikBayes-Verfahren519.549783031740343147334665899375145735406441HT030910641Lecture notes in mathematics2358(DE-605)HT001253222https://www.dietmardreier.de/annot/564C42696D677C7C393738333033313734303334337C7C434F50.jpg?sq=3rda24120920250414120158.02561639ger https://ce.visuallibrary.net//domainresource/static/graphics/connectors/viewerLogo.gif http://193.30.112.129:83/F/?func=find-c&ccl_term=ICS%3D2561639 https://ce.visuallibrary.net/id/1333805 https://ce.visuallibrary.net/i3f/v20/1333805/manifest https://ce.visuallibrary.net/proto_ftsearch/1333805