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[null,"1.","Record Nr.",null,"UNINA990002528050403321"]
[null,null,"Autore",null,"Sakamoto, Yosiyuki"]
[null,null,"Titolo",null,"Akaike information criterion statistics / Y. Sakamoto, M. Ishiguro, and G. Kitagawa"]
[null,null,"Pubbl/distr/stampa",null,"Tokio - Dordrecht : KTK Scientific Publishers D. Reidel Publishing, 1986"]
[null,null,"ISBN",null,"90-277-2253-6"]
[null,null,"Descrizione fisica",null,"XIX, 290 p. ; 22 cm"]
[null,null,"Collana",null,"Japanese series"]
[{"$nulls":4},"Mathematics and its applications"]
[null,null,"Disciplina",null,"519.5"]
[null,null,"Locazione",null,"FSPBC"]
[{"$nulls":4},"MAS"]
[null,null,"Collocazione",null,"VI E 318"]
[{"$nulls":4},"XIV-C-14"]
[{"$nulls":4},"XIV-C-13"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA990003047770403321"]
[null,null,"Titolo",null,"Differenze nazionali e capitalismo globale / a cura di Suzanne Berger e Ronald Dore ; traduzione di Bruno Grancelli, Flavia Monceri e Gianandrea Staffiero"]
[null,null,"Pubbl/distr/stampa",null,"Bologna : il Mulino, 1998"]
[null,null,"ISBN",null,"88-15-06277-7"]
[null,null,"Descrizione fisica",null,"251 p. ; 22 cm"]
[null,null,"Collana",null,"Prismi"]
[null,null,"Disciplina",null,"337"]
[null,null,"Locazione",null,"SES"]
[{"$nulls":4},"BFS"]
[{"$nulls":4},"FSPBC"]
[{"$nulls":4},"DAGEA"]
[{"$nulls":4},"DECSE"]
[null,null,"Collocazione",null,"O/1.20 BER/98"]
[{"$nulls":4},"337 BER 1"]
[{"$nulls":4},"XI A 2566"]
[{"$nulls":4},"62 382 DIF"]
[{"$nulls":4},"SE 112.03.33-"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"3.","Record Nr.",null,"UNINA9910460377903321"]
[null,null,"Autore",null,"Mahler Ronald P. S."]
[null,null,"Titolo",null,"Advances in statistical multisource-multitarget information fusion / / Ronald P.S. Mahler"]
[null,null,"Pubbl/distr/stampa",null,"Boston : , : Artech House, , ©2014"]
[{"$nulls":4},"[Piscataqay, New Jersey] : , : IEEE Xplore, , [2014]"]
[null,null,"ISBN",null,"1-60807-799-3"]
[null,null,"Descrizione fisica",null,"1 online resource (1167 p.)"]
[null,null,"Collana",null,"Artech House electronic warfare library"]
[null,null,"Disciplina",null,"006.33"]
[null,null,"Soggetti",null,"Expert systems (Computer science)"]
[{"$nulls":4},"Multisensor data fusion - Mathematics"]
[{"$nulls":4},"Bayesian statistical decision theory"]
[{"$nulls":4},"Electronic books."]
[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","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (pages 1089-1108) and index."]
[null,null,"Nota di contenuto",null,"Preface; Acknowledgments; Chapter 1 Introduction to the Book; 1.1 OVERVIEW OF FINITE-SET STATISTICS; 1.2 RECENT ADVANCES IN FINITE-SET STATISTICS; 1.3 ORGANIZATION OF THE BOOK; Part I Elements of Finite-Set Statistics; Chapter 2 Random Finite Sets; 2.1 INTRODUCTION; 2.2 SINGLE-SENSOR, SINGLE-TARGET STATISTICS; 2.3 RANDOM FINITE SETS (RFSs); 2.4 MULTIOBJECT STATISTICS IN A NUTSHELL; Chapter 3 Multiobject Calculus; 3.1 INTRODUCTION; 3.2 BASIC CONCEPTS; 3.3 SET INTEGRALS; 3.4 MULTIOBJECT DIFFERENTIAL CALCULUS; 3.5 KEY FORMULAS OF MULTIOBJECT CALCULUS."]
[{"$nulls":4},"Chapter 4 Multiobject Statistics4.1 INTRODUCTION; 4.2 BASIC MULTIOBJECT STATISTICAL DESCRIPTORS; 4.3 IMPORTANT MULTIOBJECT PROCESSES; 4.4 BASIC DERIVED RFSs; Chapter 5 Multiobject Modeling and Filtering; 5.1 INTRODUCTION; 5.2 THE MULTISENSOR-MULTITARGET BAYES FILTER; 5.3 MULTITARGET BAYES OPTIMALITY; 5.4 RFS MULTITARGET MOTION MODELS; 5.5 RFS MULTITARGET MEASUREMENT MODELS; 5.6 MULTITARGET MARKOV DENSITIES; 5.7 MULTISENSOR-MULTITARGET LIKELIHOOD FUNCTIONS; 5.8 THE MULTITARGET BAYES FILTER IN p.g.fl -- FORM; 5.9 THE FACTORED MULTITARGET BAYES FILTER; 5.10 APPROXIMATE MULTITARGET FILTERS."]
[{"$nulls":4},"Chapter 6 Multiobject Metrology6.1 INTRODUCTION; 6.2 MULTIOBJECT MISS DISTANCE; 6.3 MULTIOBJECT INFORMATION FUNCTIONALS; Part II "]
[{"$nulls":4},"RFS Filters: StandardMeasurement Model; Chapter 7 Introduction to Part II; 7.1 SUMMARY OF MAJOR LESSONS LEARNED; 7.2 STANDARD MULTITARGET MEASUREMENT MODEL; 7.3 AN APPROXIMATE STANDARD LIKELIHOOD FUNCTION; 7.4 STANDARD MULTITARGET MOTION MODEL; 7.5 STANDARD MOTION MODEL WITH TARGET SPAWNING; 7.6 ORGANIZATION OF PART II; Chapter 8 Classical PHD and CPHD Filters; 8.1 INTRODUCTION; 8.2 A GENERAL PHD FILTER; 8.3 ARBITRARY-CLUTTER PHD FILTER; 8.4 CLASSICAL PHD FILTER."]
[{"$nulls":4},"8.5 CLASSICAL CARDINALIZED PHD (CPHD) FILTER8.6 ZERO FALSE ALARMS (ZFA) CPHD FILTER; 8.7 PHD FILTER FOR STATE-DEPENDENT POISSON CLUTTER; Chapter 9 Implementing Classical PHD/CPHDFilters; 9.1 INTRODUCTION; 9.2 \"SPOOKY ACTION AT A DISTANCE\"; 9.3 MERGING AND SPLITTING FOR PHD FILTERS; 9.4 MERGING AND SPLITTING FOR CPHD FILTERS; 9.5 GAUSSIAN MIXTURE (GM) IMPLEMENTATION; 9.6 SEQUENTIAL MONTE CARLO (SMC) IMPLEMENTATION; Chapter 10 Multisensor PHD and CPHD Filters; 10.1 INTRODUCTION; 10.2 THE MULTISENSOR-MULTITARGET BAYES FILTER; 10.3 THE GENERAL MULTISENSOR PHD FILTER."]
[{"$nulls":4},"10.4 THE MULTISENSOR CLASSICAL PHD FILTER10.5 ITERATED-CORRECTOR MULTISENSOR PHD/CPHD FILTERS; 10.6 PARALLEL COMBINATION MULTISENSOR PHD AND CPHD FILTERS; 10.7 AN ERRONEOUS \"AVERAGED\" MULTISENSOR PHD FILTER; 10.8 PERFORMANCE COMPARISONS; Chapter 11 Jump-Markov PHD/CPHD Filters; 11.1 INTRODUCTION; 11.2 JUMP-MARKOV FILTERS: A REVIEW; 11.3 MULTITARGET JUMP-MARKOV SYSTEMS; 11.4 JUMP-MARKOV PHD FILTER; 11.5 JUMP-MARKOV CPHD FILTER; 11.6 VARIABLE STATE SPACE JUMP-MARKOV CPHD FILTERS; 11.7 IMPLEMENTING JUMP-MARKOV PHD/CPHD FILTERS; 11.8 IMPLEMENTED JUMP-MARKOV PHD/CPHD FILTERS."]
[null,null,null,"Sommario/riassunto","This is the sequel to the 2007 Artech House bestselling title, Statistical Multisource-Multitarget Information Fusion. That earlier book was a comprehensive resource for an in-depth understanding of finite-set statistics (FISST), a unified, systematic, and Bayesian approach to information fusion. The cardinalized probability hypothesis density (CPHD) filter, which was first systematically described in the earlier book, has since become a standard multitarget detection and tracking technique, especially in research and development. Since 2007, FISST has inspired a considerable amount of research."]