Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition) - Softcover

Sucar, Luis Enrique Enrique

Softcover

ISBN 10: 1447170547 ISBN 13: 9781447170549

Verlag: Springer, 2016

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Reseña del editor

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.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2016
• ISBN 10: 1447170547
• ISBN 13: 9781447170549
• Einband: Tapa blanda
• Sprache: Englisch
• Anzahl der Seiten: 280