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 applicat...
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| Định dạng: | Sách |
| Ngôn ngữ: | English |
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Springer
2015
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| Truy cập trực tuyến: | https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/58557 |
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oai:scholar.dlu.edu.vn:DLU123456789-585572023-11-11T06:13:20Z Probabilistic Graphical Models: Principles and Applications Sucar, Luis Enrique Probability Graphical modeling Uncertainty Computer Science Statistics 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. 2015-09-29T00:55:49Z 2015-09-29T00:55:49Z 2015 Book 978-1-4471-6699-3 978-1-4471-6698-6 https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/58557 en application/pdf Springer |
| institution |
Thư viện Trường Đại học Đà Lạt |
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Thư viện số |
| language |
English |
| topic |
Probability Graphical modeling Uncertainty Computer Science Statistics |
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Probability Graphical modeling Uncertainty Computer Science Statistics Sucar, Luis Enrique Probabilistic Graphical Models: Principles and Applications |
| description |
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. |
| format |
Book |
| author |
Sucar, Luis Enrique |
| author_facet |
Sucar, Luis Enrique |
| author_sort |
Sucar, Luis Enrique |
| title |
Probabilistic Graphical Models:
Principles and Applications |
| title_short |
Probabilistic Graphical Models:
Principles and Applications |
| title_full |
Probabilistic Graphical Models:
Principles and Applications |
| title_fullStr |
Probabilistic Graphical Models:
Principles and Applications |
| title_full_unstemmed |
Probabilistic Graphical Models:
Principles and Applications |
| title_sort |
probabilistic graphical models:
principles and applications |
| publisher |
Springer |
| publishDate |
2015 |
| url |
https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/58557 |
| _version_ |
1782539854332035072 |