LEADER 05133nam 22007695 450 001 9910299226403321 005 20200702052139.0 010 $a1-4471-6699-X 024 7 $a10.1007/978-1-4471-6699-3 035 $a(CKB)3710000000436779 035 $a(SSID)ssj0001558352 035 $a(PQKBManifestationID)16183232 035 $a(PQKBTitleCode)TC0001558352 035 $a(PQKBWorkID)14819141 035 $a(PQKB)11660275 035 $a(DE-He213)978-1-4471-6699-3 035 $a(MiAaPQ)EBC6315310 035 $a(MiAaPQ)EBC5575373 035 $a(Au-PeEL)EBL5575373 035 $a(OCoLC)911923420 035 $a(PPN)186400446 035 $a(EXLCZ)993710000000436779 100 $a20150619d2015 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aProbabilistic Graphical Models$b[electronic resource] $ePrinciples and Applications /$fby Luis Enrique Sucar 205 $a1st ed. 2015. 210 1$aLondon :$cSpringer London :$cImprint: Springer,$d2015. 215 $a1 recurso en línea (xxiv, 253 páginas) 225 1 $aAdvances in Computer Vision and Pattern Recognition,$x2191-6586 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a1-4471-6698-1 327 $aPart 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. 330 $aThis accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Topics and features: Presents a unified framework encompassing all of the main classes of PGMs Explores the fundamental aspects of representation, inference and learning for each technique Describes the practical application of the different techniques Examines the latest developments in the field, covering multidimensional Bayesian classifiers, relational graphical models and causal models Provides exercises, suggestions for further reading, and ideas for research or programming projects at the end of each chapter Suggests possible course outlines for instructors in the preface This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference. Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. 410 0$aAdvances in Computer Vision and Pattern Recognition,$x2191-6586 606 $aMathematical statistics 606 $aArtificial intelligence 606 $aPattern recognition 606 $aProbabilities 606 $aElectrical engineering 606 $aProbability and Statistics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17036 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004 606 $aElectrical Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T24000 615 0$aMathematical statistics. 615 0$aArtificial intelligence. 615 0$aPattern recognition. 615 0$aProbabilities. 615 0$aElectrical engineering. 615 14$aProbability and Statistics in Computer Science. 615 24$aArtificial Intelligence. 615 24$aPattern Recognition. 615 24$aProbability Theory and Stochastic Processes. 615 24$aElectrical Engineering. 676 $a004 700 $aSucar$b Luis Enrique$4aut$4http://id.loc.gov/vocabulary/relators/aut$01060261 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299226403321 996 $aProbabilistic Graphical Models$92512048 997 $aUNINA