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020 _a9781447166993
_z978-1-4471-6699-3
024 7 _a10.1007/978-1-4471-6699-3
_2doi
050 4 _aQA276-280
072 7 _aUYAM
_2bicssc
072 7 _aCOM077000
_2bisacsh
072 7 _aUYAM
_2thema
072 7 _aUFM
_2thema005.55
_223
100 1 _aSucar, Luis Enrique.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aProbabilistic Graphical Models :
_bPrinciples and Applications /
_cby Luis Enrique Sucar.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2015.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 0 _aAdvances in Computer Vision and Pattern Recognition,
_x2191-6586
505 0 _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.
520 _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.
650 0 _aComputer science.
650 0 _aArtificial intelligence.
650 0 _aOptical pattern recognition.
650 0 _aDistribution (Probability theory.
650 0 _aComputer engineering.
650 1 4 _aProbability and Statistics in Computer Science.
_0http://scigraph.springernature.com/things/product-market-codes/I17036
650 2 4 _aArtificial Intelligence.
_0http://scigraph.springernature.com/things/product-market-codes/I21000
650 2 4 _aPattern Recognition.
_0http://scigraph.springernature.com/things/product-market-codes/I2203X
650 2 4 _aProbability Theory and Stochastic Processes.
_0http://scigraph.springernature.com/things/product-market-codes/M27004
650 2 4 _aElectrical Engineering.
_0http://scigraph.springernature.com/things/product-market-codes/T24000
710 2 _aSpringerLink (Online service)
856 4 0 _uhttps://doi.org/10.1007/978-1-4471-6699-3
_3Springer eBooks
_zOnline access link to the resource
912 _aZDB-2-SCS
999 _c200434077
_d52289
942 _2lcc
_cEBK
041 _aeng