Parametri
- 277pagine
- 10 ore di lettura
Maggiori informazioni sul libro
This accessible text/reference offers a comprehensive introduction to probabilistic graphical models (PGMs) from an engineering viewpoint. It covers the fundamentals of the main classes of PGMs, including representation, inference, and learning principles, while reviewing real-world applications across various disciplines. The book highlights the uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Key features include a unified framework for all main PGM classes, exploration of fundamental aspects for each technique, and practical applications. It also examines recent developments such as multidimensional Bayesian classifiers, relational graphical models, and causal models. Each chapter concludes with exercises, further reading suggestions, and ideas for research or programming projects, along with possible course outlines for instructors provided in the preface. This classroom-tested resource serves as a textbook for advanced undergraduate or graduate courses in probabilistic graphical models for students in computer science, engineering, and physics. It is also an invaluable reference for professionals looking to apply these models in their fields or seeking to understand the foundational techniques.
Acquisto del libro
Probabilistic Graphical Models, Luis Enrique Sucar
- Lingua
- Pubblicato
- 2015
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- (Copertina rigida)
Metodi di pagamento
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- Titolo
- Probabilistic Graphical Models
- Sottotitolo
- Principles and Applications
- Lingua
- Inglese
- Autori
- Luis Enrique Sucar
- Editore
- Springer London
- Pubblicato
- 2015
- Formato
- Copertina rigida
- Pagine
- 277
- ISBN10
- 1447166981
- ISBN13
- 9781447166986
- Serie
- Descrizione
- This accessible text/reference offers a comprehensive introduction to probabilistic graphical models (PGMs) from an engineering viewpoint. It covers the fundamentals of the main classes of PGMs, including representation, inference, and learning principles, while reviewing real-world applications across various disciplines. The book highlights the uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Key features include a unified framework for all main PGM classes, exploration of fundamental aspects for each technique, and practical applications. It also examines recent developments such as multidimensional Bayesian classifiers, relational graphical models, and causal models. Each chapter concludes with exercises, further reading suggestions, and ideas for research or programming projects, along with possible course outlines for instructors provided in the preface. This classroom-tested resource serves as a textbook for advanced undergraduate or graduate courses in probabilistic graphical models for students in computer science, engineering, and physics. It is also an invaluable reference for professionals looking to apply these models in their fields or seeking to understand the foundational techniques.