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 ...
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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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Add this copy of Bayesian Nonparametric Statistics: École D'Été De to cart. $43.71, good condition, Sold by Marlton Books LLC rated 4.0 out of 5 stars, ships from new york, NY, UNITED STATES, published 2024 by Springer International Publishing AG.
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New. Print on demand Contains: Illustrations, black & white, Illustrations, color. École d'Été de Probabilités de Saint-Flour ; Lecture Notes in Mathematics . XII, 216 p. 14 illus., 7 illus. in color. Intended for professional and scholarly audience.
