{"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":28,"n_cols":5,"rows_kept":28,"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],[11,2,11,3],[12,2,12,3],[13,2,13,3],[14,2,14,3],[17,2,17,3],[18,2,18,3],[22,2,22,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9911020439303321"]
[null,null,"Autore",null,"Cairoli R (Renzo), <1931-1994.>"]
[null,null,"Titolo",null,"Sequential stochastic optimization / / R. Cairoli, Robert C. Dalang"]
[null,null,"Pubbl/distr/stampa",null,"New York, : J. Wiley & Sons, c1996"]
[null,null,"ISBN",null,"9786613227997"]
[{"$nulls":4},"9781283227995"]
[{"$nulls":4},"1283227991"]
[{"$nulls":4},"9781118164396"]
[{"$nulls":4},"1118164393"]
[{"$nulls":4},"9781118164402"]
[{"$nulls":4},"1118164407"]
[null,null,"Descrizione fisica",null,"1 online resource (348 p.)"]
[null,null,"Collana",null,"Wiley series in probability and mathematical statistics"]
[null,null,"Disciplina",null,"519.2"]
[null,null,"Soggetti",null,"Optimal stopping (Mathematical statistics)"]
[{"$nulls":4},"Dynamic programming"]
[{"$nulls":4},"Stochastic control theory"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","\"A Wiley-Interscience publication.\""]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and indexes."]
[null,null,"Nota di contenuto",null,"Sequential Stochastic Optimization; Contents; Preface; Notation and Conventions; 1. Preliminaries; 1.1 Filtered Probability Spaces; 1.2 Random Variables; 1.3 Stopping Points; 1.4 Increasing Paths and Accessible Stopping Points; 1.5 Some Operations on Accessible Stopping Points; 1.6 Stochastic Processes and Martingales; Exercises; Historical Notes; 2. Sums of Independent Random Variables; 2.1 Maximal Inequalities; 2.2 Integrability Criteria for the Supremum; 2.3 The Strong Law of Large Numbers; 2.4 Case Where the Random Variables Are Identically Distributed; Exercises; Historical Notes"]
[{"$nulls":4},"3. Optimal Stopping3.1 Stating the Problem; 3.2 Snell's Envelope; 3.3 Solving the Problem; 3.4 A Related Problem; 3.5 Maximal Accessible Stopping Points; 3.6 Case Where the Index Set is Finite; 3.7 An Application to Normalized Partial Sums; 3.8 Complements; Exercises; Historical Notes; 4. Reduction to a Single Dimension; 4.1 Linear Representation of Accessible Stopping Points; 4.2 Applications; 4.3 Linear Representation in the Setting of Inaccessible Stopping Points; Exercises; Historical Notes; 5. Accessibility and Filtration Structure; 5.1 Conditions for Accessibility"]
[{"$nulls":4},"5.2 Consequences for the Structure of the Filtration5.3 The Bidimensional Case; 5.4 Predictability of Optional Increasing Paths; 5.5 The Combinatorial Structure of a Filtration; 5.6 The Combinatorial Structure of a Filtration Satisfying COl; 5.7 Optimal Stopping and Linear Optimization; Exercises; Historical Notes; 6. Sequential Sampling; 6.1 Stating the Problem; 6.2 Constructing the Model; 6.3 The Reward Process and Snell's Envelope; 6.4 Describing the Optimal Strategy; 6.5 The Likelihood-Ratio Test; 6.6 Applications; 6.7 Complement; Exercises; Historical Notes; 7. Optimal Sequential Control"]
[{"$nulls":4},"7.1 An Example7.2 Preliminaries; 7.3 Controls; 7.4 Optimization; 7.5 Optimization Over Finite Controls; 7.6 Case Where the Index Set Is Finite; 7.7 Extension to General Index Sets; Exercises; Historical Notes; 8. Multiarmed Bandits; 8.1 Formulating the Problem; 8.2 Index Controls; 8.3 Gittins Indices; 8.4 Characterizing Optimal Controls; 8.5 Examples; Exercises; Historical Notes; 9. The Markovian Case; 9.1 Markov Chains and Superharmonic Functions; 9.2 Optimal Control of a Markov Chain; 9.3 The Special Case of a Random Walk"]
[{"$nulls":4},"9.4 Control and Stopping at the Time of First Visit to a Set of States9.5 Markov Structures; Exercises; Historical Notes; 10. Optimal Switching Between Two Random Walks; 10.1 Formulating and Solving the Problem; 10.2 Some Properties of the Solution; 10.3 The Structure of the Solution; 10.4 Constructing the Switching Curves; 10.5 Characterizing the Type of the Solution; 10.6 Determining the Type of the Solution; Exercises; Historical Notes; Bibliography; Index of Notation; Index of Terms"]
[null,null,null,"Sommario/riassunto","Sequential Stochastic Optimization provides mathematicians and applied researchers with a well-developed framework in which stochastic optimization problems can be formulated and solved. Offering much material that is either new or has never before appeared in book form, it lucidly presents a unified theory of optimal stopping and optimal sequential control of stochastic processes. This book has been carefully organized so that little prior knowledge of the subject is assumed; its only prerequisites are a standard graduate course in probability theory and some familiarity with discrete-paramet"]