05133nam 22007695 450 991029922640332120200702052139.01-4471-6699-X10.1007/978-1-4471-6699-3(CKB)3710000000436779(SSID)ssj0001558352(PQKBManifestationID)16183232(PQKBTitleCode)TC0001558352(PQKBWorkID)14819141(PQKB)11660275(DE-He213)978-1-4471-6699-3(MiAaPQ)EBC6315310(MiAaPQ)EBC5575373(Au-PeEL)EBL5575373(OCoLC)911923420(PPN)186400446(EXLCZ)99371000000043677920150619d2015 u| 0engurnn#008mamaatxtccrProbabilistic Graphical Models[electronic resource] Principles and Applications /by Luis Enrique Sucar1st ed. 2015.London :Springer London :Imprint: Springer,2015.1 recurso en línea (xxiv, 253 páginas)Advances in Computer Vision and Pattern Recognition,2191-6586Bibliographic Level Mode of Issuance: Monograph1-4471-6698-1 Part I: Fundamentals -- Introduction -- Probability Theory -- Graph Theory -- Part II: Probabilistic Models -- Bayesian Classifiers -- Hidden Markov Models -- Markov Random Fields -- Bayesian Networks: Representation and Inference -- Bayesian Networks: Learning -- Dynamic and Temporal Bayesian Networks -- Part III: Decision Models -- Decision Graphs -- Markov Decision Processes -- Part IV: Relational and Causal Models -- Relational Probabilistic Graphical Models -- Graphical Causal Models.This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Topics and features: Presents a unified framework encompassing all of the main classes of PGMs Explores the fundamental aspects of representation, inference and learning for each technique Describes the practical application of the different techniques Examines the latest developments in the field, covering multidimensional Bayesian classifiers, relational graphical models and causal models Provides exercises, suggestions for further reading, and ideas for research or programming projects at the end of each chapter Suggests possible course outlines for instructors in the preface This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference. Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico.Advances in Computer Vision and Pattern Recognition,2191-6586Mathematical statisticsArtificial intelligencePattern recognitionProbabilitiesElectrical engineeringProbability and Statistics in Computer Sciencehttps://scigraph.springernature.com/ontologies/product-market-codes/I17036Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XProbability Theory and Stochastic Processeshttps://scigraph.springernature.com/ontologies/product-market-codes/M27004Electrical Engineeringhttps://scigraph.springernature.com/ontologies/product-market-codes/T24000Mathematical statistics.Artificial intelligence.Pattern recognition.Probabilities.Electrical engineering.Probability and Statistics in Computer Science.Artificial Intelligence.Pattern Recognition.Probability Theory and Stochastic Processes.Electrical Engineering.004Sucar Luis Enriqueauthttp://id.loc.gov/vocabulary/relators/aut1060261MiAaPQMiAaPQMiAaPQBOOK9910299226403321Probabilistic Graphical Models2512048UNINA01221oam 2200241z- 450 991014956690332120230906203136.01-5071-4323-0(CKB)3710000000934947(BIP)055827609(EXLCZ)99371000000093494720210505c2016uuuu -u- -engIMAGINEUNITEXTO. Digital Publishing1 online resource (71 p.) John Lennon was a musician, singer, songwriter, member of The Beatles, and Knight of the British Empire. He also is one of the maximum music icons of the 20th century. His rejection to the established values and his innovative capability in the musical and personal level are a source of inspiration to every generation that finds within his life a role model in the search of the rupture of the old patterns and in the construction of a new future. In this book John tells stories and situations of his life along with songs written by him which describe his life even better than his stories.Droznes Lázaro1255430Moreno Costilla MoiséstrlBOOK9910149566903321IMAGINE3581945UNINA