Probabilistic graphical models [electronic resource] : principles and applications / Luis Enrique Sucar.
Sucar, Luis Enrique, author.
2015
Q375 .S82 2015eb
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Title
Probabilistic graphical models [electronic resource] : principles and applications / Luis Enrique Sucar.
Author
Sucar, Luis Enrique, author.
ISBN
9781447166993 electronic book
144716699X electronic book
9781447166986
DOI
https://doi.org/10.1007/978-1-4471-6699-3
Published
London : Springer, 2015.
Language
English
Description
1 online resource (xxiv, 253 pages) : illustrations.
Item Number
10.1007/978-1-4471-6699-3 doi
Call Number
Q375 .S82 2015eb
Dewey Decimal Classification
003/.54
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed June 26, 2015).
Series
Advances in computer vision and pattern recognition.
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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.

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