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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| Format: | eBook |
| Language: | English |
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London
Springer London
2015, 2015
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| Edition: | 1st ed. 2015 |
| Series: | Advances in Computer Vision and Pattern Recognition
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 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