{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":30,"n_cols":5,"rows_kept":30,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[4,2,4,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[10,2,10,3],[24,2,24,3],[25,2,25,3],[27,2,27,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNISA996630872303316"]
[null,null,"Autore",null,"Castillo Ismaël"]
[null,null,"Titolo",null,"Bayesian Nonparametric Statistics : École d’Été de Probabilités de Saint-Flour LI - 2023 / / by Ismaël Castillo"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"9783031740350"]
[{"$nulls":4},"9783031740343"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (225 pages)"]
[null,null,"Collana",null,"École d'Été de Probabilités de Saint-Flour ; ; 2358"]
[null,null,"Disciplina",null,"519.5"]
[null,null,"Soggetti",null,"Statistics"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Mathematical optimization"]
[{"$nulls":4},"Calculus of variations"]
[{"$nulls":4},"Statistical physics"]
[{"$nulls":4},"Probabilities"]
[{"$nulls":4},"Statistical Theory and Methods"]
[{"$nulls":4},"Machine Learning"]
[{"$nulls":4},"Calculus of Variations and Optimization"]
[{"$nulls":4},"Statistical Physics"]
[{"$nulls":4},"Probability Theory"]
[{"$nulls":4},"Estadística bayesiana"]
[{"$nulls":4},"Estadística no paramètrica"]
[{"$nulls":4},"Llibres electrònics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"-1. Introduction, rates I.-2. Rates II and first examples.-3. Adaptation I: smoothness.-4. Adaptation II: high-dimensions and deep neural networks -- 5. Bernstein-von Mises I: functionals -- 6. Bernstein-von Mises II: multiscale and applications -- 7. classification and multiple testing -- 8. Variational approximations."]
[null,null,null,"Sommario/riassunto","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 "]
[{"$nulls":4},"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. ."]