{"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":25,"n_cols":5,"rows_kept":25,"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],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[20,2,20,3],[21,2,21,3],[23,2,23,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNISA996655263503316"]
[null,null,"Autore",null,"Chewi Sinho"]
[null,null,"Titolo",null,"Statistical Optimal Transport : École d'Été de Probabilités de Saint-Flour XLIX – 2019 / / by Sinho Chewi, Jonathan Niles-Weed, Philippe Rigollet"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"3-031-85160-9"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XIV, 260 p. 10 illus., 3 illus. in color.)"]
[null,null,"Collana",null,"École d'Été de Probabilités de Saint-Flour ; ; 2364"]
[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"]
[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. Optimal Transport -- 2. Estimation of Wasserstein distances -- 3. Estimation of transport maps -- 4. Entropic optimal transport -- 5. Wasserstein gradient flows: theory.-6. Wasserstein gradient flows: applications -- 7. Metric geometry of the Wasserstein space -- 8. Wasserstein barycenters."]
[null,null,null,"Sommario/riassunto","This monograph aims to offer a concise introduction to optimal transport, quickly transitioning to its applications in statistics and machine learning. It is primarily tailored for students and researchers in these fields, yet it remains accessible to a broader audience of applied mathematicians and computer scientists. Each chapter is complemented with exercises for the reader to test their understanding. As such, this monograph is suitable for a graduate course on the topic of statistical optimal transport."]