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Probabilistic graphical models = pri...

Sucar, Luis Enrique.

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Probabilistic graphical models = principles and applications /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Probabilistic graphical models/ by Luis Enrique Sucar.
Reminder of title:	principles and applications /
Author:	Sucar, Luis Enrique.
Published:	London :Springer London : : 2015.,
Description:	xxiv, 253 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Uncertainty (Information theory) -
Online resource:	http://dx.doi.org/10.1007/978-1-4471-6699-3
ISBN:	9781447166993 (electronic bk.)

Probabilistic graphical models = principles and applications /
Sucar, Luis Enrique.

Probabilistic graphical modelsprinciples and applications /[electronic resource] :by Luis Enrique Sucar. - London :Springer London :2015. - xxiv, 253 p. :ill. (some col.), digital ;24 cm. - Advances in computer vision and pattern recognition,2191-6586. - Advances in computer vision and pattern recognition..

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.

ISBN: 9781447166993 (electronic bk.)

Standard No.: 10.1007/978-1-4471-6699-3doiSubjects--Topical Terms:

587701
Uncertainty (Information theory)

LC Class. No.: Q375

Dewey Class. No.: 003.54

Probabilistic graphical models = principles and applications /
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490 1 $a Advances in computer vision and pattern recognition, $x 2191-6586
505 0 $a 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.
520 $a 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.
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