Probabilistic Graphical Models

Principles and Applications

Luis Enrique Sucar (Autor)

Buch | Softcover

253 Seiten

2016 | Softcover reprint of the original 1st ed. 2015
Springer London Ltd (Verlag)
978-1-4471-7054-9 (ISBN)

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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. Features: presents a unified framework encompassing all of the main classes of PGMs; 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.

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.

Erscheinungsdatum	30.10.2016
Reihe/Serie	Advances in Pattern Recognition
Zusatzinfo	4 Illustrations, color; 113 Illustrations, black and white; XXIV, 253 p. 117 illus., 4 illus. in color.
Verlagsort	England
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
	Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
	Technik ► Elektrotechnik / Energietechnik
Schlagworte	bayesian classifiers • Bayesian networks • decision networks • hidden Markov models • Influence Diagrams • Learning Graphical Models • Markov Decision Processes • Markov Random Fields • Probabilistic Graphical Models • probabilistic inference
ISBN-10	1-4471-7054-7 / 1447170547
ISBN-13	978-1-4471-7054-9 / 9781447170549
Zustand	Neuware