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Probabilistic Graphical Models
ISBN-13:
9783030619435
Veröffentl:
2020
Seiten:
355
Autor:
Luis Enrique Sucar
Serie:
Advances in Computer Vision and Pattern Recognition
eBook Typ:
PDF
eBook Format:
EPUB
Kopierschutz:
1 - PDF Watermark
Sprache:
Englisch
Beschreibung:
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.
Introduction.- Probability Theory.- Graph Theory.- Bayesian Classifiers.- Hidden Markov Models.- Markov Random Fields.- Bayesian Networks: Representation and Inference.- Bayesian Networks: Learning.- Dynamic and Temporal Bayesian Networks.- Decision Graphs.- Markov Decision Processes.- Partially Observable Markov Decision Processes.- Relational Probabilistic Graphical Models.- Graphical Causal Models.- Causal Discovery.- Deep Learning and Graphical Models.- A Python Library for Inference and Learning.- Glossary.- Index
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