03116nam 2200637 450 991048298750332120220821131144.01-280-85335-297866108533593-540-48511-210.1007/978-3-540-48511-7(CKB)1000000000282793(EBL)3036614(SSID)ssj0000156160(PQKBManifestationID)11156471(PQKBTitleCode)TC0000156160(PQKBWorkID)10123745(PQKB)10760016(DE-He213)978-3-540-48511-7(MiAaPQ)EBC3036614(MiAaPQ)EBC6812108(Au-PeEL)EBL6812108(OCoLC)1287133977(PPN)123158176(EXLCZ)99100000000028279320220821d2007 uy 0engur|n|---|||||txtccrFluctuation theory for Lévy processes Ecole d'Eté de Probabilités de Saint-Flour XXXV - 2005 /edited by Jean Picard and Ronald A. Doney1st ed. 2007.Berlin, Heidelberg :Springer-Verlag,[2007]©20071 online resource (153 p.)École d'Été de Probabilités de Saint-Flour,0721-5363 ;1897Description based upon print version of record.3-540-48510-4 Includes bibliographical references (p. [133]-137) and index.to Lévy Processes -- Subordinators -- Local Times and Excursions -- Ladder Processes and the Wiener–Hopf Factorisation -- Further Wiener–Hopf Developments -- Creeping and Related Questions -- Spitzer's Condition -- Lévy Processes Conditioned to Stay Positive -- Spectrally Negative Lévy Processes -- Small-Time Behaviour.Lévy processes, i.e. processes in continuous time with stationary and independent increments, are named after Paul Lévy, who made the connection with infinitely divisible distributions and described their structure. They form a flexible class of models, which have been applied to the study of storage processes, insurance risk, queues, turbulence, laser cooling, ... and of course finance, where the feature that they include examples having "heavy tails" is particularly important. Their sample path behaviour poses a variety of difficult and fascinating problems. Such problems, and also some related distributional problems, are addressed in detail in these notes that reflect the content of the course given by R. Doney in St. Flour in 2005.École d'Été de Probabilités de Saint-Flour,0721-5363 ;1897Lévy processesLévy processes.519.282Picard Jean1959-Doney Ronald A.Ecole d'été de probabilités de Saint-Flour(35th :2005)MiAaPQMiAaPQMiAaPQBOOK9910482987503321Fluctuation theory for Lévy processes2905564UNINA02568nam 2200421 450 991063397710332120231027112707.01-83969-888-810.5772/intechopen.95124(CKB)5700000000338589(NjHacI)995700000000338589(EXLCZ)99570000000033858920230330d2022 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierData Clustering /edited by Niansheng TangLondon :IntechOpen,2022.1 online resource (126 pages)Artificial intelligence1-83969-887-X 1. Introductory Chapter: Development of Data Clustering -- 2. Clustering Algorithms: An Exploratory Review -- 3. Clustering by Similarity of Brazilian Legal Documents Using Natural Language Processing Approaches -- Assessing Heterogeneity of Two-Part Model via Bayesian Model-Based Clustering with Its Application to Cocaine Use Data -- 5. Application of Jump Diffusion Models in Insurance Claim Estimation -- 6. Fuzzy Perceptron Learning for Non-Linearly Separable Patterns -- . Semantic Map: Bringing Together Groups and Discourses.In view of the considerable applications of data clustering techniques in various fields, such as engineering, artificial intelligence, machine learning, clinical medicine, biology, ecology, disease diagnosis, and business marketing, many data clustering algorithms and methods have been developed to deal with complicated data. These techniques include supervised learning methods and unsupervised learning methods such as density-based clustering, K-means clustering, and K-nearest neighbor clustering. This book reviews recently developed data clustering techniques and algorithms and discusses the development of data clustering, including measures of similarity or dissimilarity for data clustering, data clustering algorithms, assessment of clustering algorithms, and data clustering methods recently developed for insurance, psychology, pattern recognition, and survey data.Artificial intelligence (IntechOpen (Firm))Artificial intelligenceCluster analysisArtificial intelligence.Cluster analysis.006.3Tang NianshengNjHacINjHaclBOOK9910633977103321Data Clustering2687086UNINA