Probabilistic graphical models : principles and applications /
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 applicati...
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| Format: | Book |
| Language: | English |
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London ; New York :
Springer,
[2015]
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| Series: | Advances in computer vision and pattern recognition.
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| 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. 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. |
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| Physical Description: | xxiv, 253 pages : illustrations ; 25 cm. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 1447166981 9781447166986 |