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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) - Hardcover
Synopsis
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...
About the Author
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.
"About this title" may belong to another edition of this title.
- PublisherThe MIT Press
- Publication date2009
- ISBN 10 0262013193
- ISBN 13 9780262013192
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages1231
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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Published by
The MIT Press (edition 1), 2009
ISBN 10: 0262013193
ISBN 13: 9780262013192
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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Published by
The MIT Press (edition 1), 2009
ISBN 10: 0262013193
ISBN 13: 9780262013192
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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Published by
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ISBN 10: 0262013193
ISBN 13: 9780262013192
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