Items related to Learning in Graphical Models (Adaptive Computation...
Synopsis
Graphical models, a marriage between probability theory and graph theory, provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering―uncertainty and complexity. In particular, they play an increasingly important role in the design and analysis of machine learning algorithms. Fundamental to the idea of a graphical model is the notion of modularity: a complex system is built by combining simpler parts. Probability theory serves...
About the Author
Michael I. Jordan is Professor of Computer Science and of Statistics at the University of California, Berkeley, and recipient of the ACM/AAAI Allen Newell Award.
Review
This book deals with an area that is central to modern statistical science and which has also attracted the interest of outstanding researchers beyond the statistical mainstream, from computer science, and neural computing. The book gives a vital and timely overview of current work at this interface, described by contributors representing the complete spectrum of backgrounds.
―Michael Titterington, Professor of Statistics, University of GlasgowLearning in Graphical Models is the product of...
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- PublisherBradford Books
- Publication date1998
- ISBN 10 0262600323
- ISBN 13 9780262600323
- BindingPaperback
- LanguageEnglish
- Edition number1
- Number of pages644
- EditorJordan Michael Irwin
- Rating
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Learning in Graphical Models
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Learning in Graphical Models (Adaptive Computation and Machine Learning)
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Learning in Graphical Models (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series)
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