Probabilistic Graphical Models
principles and techniques
$325.78
- Hardcover
1270 pages
- Release Date
30 July 2009
Summary
A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.Most tasks require a person or an automated system to reason-to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by …
Book Details
| ISBN-13: | 9780262013192 |
|---|---|
| ISBN-10: | 0262013193 |
| Series: | Adaptive Computation and Machine Learning series |
| Author: | Daphne Koller, Nir Friedman |
| Publisher: | MIT Press Ltd |
| Imprint: | MIT Press |
| Format: | Hardcover |
| Number of Pages: | 1270 |
| Release Date: | 30 July 2009 |
| Weight: | 2.13kg |
| Dimensions: | 229mm x 203mm x 43mm |
What They're Saying
Critics Review
“This landmark book provides a very extensive coverage of the field, ranging from basic representational issues to the latest techniques for approximate inference and learning. As such, it is likely to become a definitive reference for all those who work in this area. Detailed worked examples and case studies also make the book accessible to students.”–Kevin Murphy, Department of Computer Science, University of British Columbia
About The Author
Daphne Koller
Daphne Koller is Professor in the Department of Computer Science at Stanford University.Nir Friedman is Professor in the Department of Computer Science and Engineering at Hebrew University.
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