03774nam 2200637 a 450 991078855520332120230725045527.01-283-14441-79786613144416981-4299-88-X(CKB)3360000000001365(EBL)731162(OCoLC)741492811(SSID)ssj0000634096(PQKBManifestationID)12207111(PQKBTitleCode)TC0000634096(PQKBWorkID)10622242(PQKB)11008330(MiAaPQ)EBC731162(WSP)00007699(Au-PeEL)EBL731162(CaPaEBR)ebr10480285(CaONFJC)MIL314441(EXLCZ)99336000000000136520110712d2011 uy 0engur|n|---|||||txtccrDependence modeling[electronic resource] vine copula handbook /editors, Dorota Kurowicka, Harry JoeHackensack, N.J. World Scientific20111 online resource (368 p.)Description based upon print version of record.981-4299-87-1 Includes bibliographical references and index.Preface; Contents; 1. Introduction: Dependence Modeling D. Kurowicka; 2. Multivariate Copulae M. Fischer; 3. Vines Arise R. M. Cooke, H. Joe and K. Aas; 4. Sampling Count Variables with Specified Pearson Correlation: A Comparison between a Naive and a C-Vine Sampling Approach V. Erhardt and C. Czado; 5. Micro Correlations and Tail Dependence R. M. Cooke, C. Kousky and H. Joe; 6. The Copula Information Criterion and Its Implications for the Maximum Pseudo-Likelihood Estimator S. Grønneberg; 7. Dependence Comparisons of Vine Copulae with Four or More Variables H. Joe8. Tail Dependence in Vine Copulae H. Joe9. Counting Vines O. Morales-Napoles; 10. Regular Vines: Generation Algorithm and Number of Equivalence Classes H. Joe, R. M. Cooke and D. Kurowicka; 11. Optimal Truncation of Vines D. Kurowicka; 12. Bayesian Inference for D-Vines: Estimation and Model Selection C. Czado and A. Min; 13. Analysis of Australian Electricity Loads Using Joint Bayesian Inference of D-Vines with Autoregressive Margins C. Czado, F. G ̈artner and A. Min; 14. Non-Parametric Bayesian Belief Nets versus Vines A. Hanea15. Modeling Dependence between Financial Returns Using Pair-Copula Constructions K. Aas and D. Berg16. Dynamic D-Vine Model A. Heinen and A. Valdesogo; 17. Summary and Future Directions D. Kurowicka; IndexThis book is a collaborative effort from three workshops held over the last three years, all involving principal contributors to the vine-copula methodology. Research and applications in vines have been growing rapidly and there is now a growing need to collate basic results, and standardize terminology and methods. Specifically, this handbook will trace historical developments, standardizing notation and terminology, summarize results on bivariate copulae, summarize results for regular vines, and give an overview of its applications. In addition, many of these results are new and not readily Copulas (Mathematical statistics)Dependence (Statistics)Distribution (Probability theory)Copulas (Mathematical statistics)Dependence (Statistics)Distribution (Probability theory)519.5Kurowicka Dorota474601Joe Harry411519MiAaPQMiAaPQMiAaPQBOOK9910788555203321Dependence modeling3676432UNINA03062nam 22005895 450 991041344100332120250609110037.0981-15-4584-710.1007/978-981-15-4584-9(CKB)4100000011343598(DE-He213)978-981-15-4584-9(MiAaPQ)EBC6272301(PPN)253256801(MiAaPQ)EBC6263953(EXLCZ)99410000001134359820200713d2020 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierThe Development of Deep Learning Technologies Research on the Development of Electronic Information Engineering Technology in China1st ed. 2020.Singapore :Springer Singapore :Imprint: Springer,2020.1 online resource (XIV, 58 p. 15 illus., 13 illus. in color.)981-15-4583-9 Includes bibliographical references.Chapter 1: Deep Learning: History and State-of-the-arts -- Chapter 2: Deep Learning Development Status in China -- Chapter 3: Future and Discussions.This book is a part of the Blue Book series “Research on the Development of Electronic Information Engineering Technology in China,” which explores the cutting edge of deep learning studies. A subfield of machine learning, deep learning differs from conventional machine learning methods in its ability to learn multiple levels of representation and abstraction by using several layers of nonlinear modules for feature extraction and transformation. The extensive use and huge success of deep learning in speech, CV, and NLP have led to significant advances toward the full materialization of AI. Focusing on the development of deep learning technologies, this book also discusses global trends, the status of deep learning development in China and the future of deep learning.Artificial intelligenceComputer scienceComputer industryArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Popular Computer Sciencehttps://scigraph.springernature.com/ontologies/product-market-codes/Q23000The Computer Industryhttps://scigraph.springernature.com/ontologies/product-market-codes/I24016Asian Economicshttps://scigraph.springernature.com/ontologies/product-market-codes/W45010AsiaEconomic conditionsArtificial intelligence.Computer science.Computer industry.Artificial Intelligence.Popular Computer Science.The Computer Industry.Asian Economics.016.403MiAaPQMiAaPQMiAaPQBOOK9910413441003321The Development of Deep Learning Technologies1990828UNINA