[PDF.75eb] Learning in Graphical Models (Adaptive Computation and Machine Learning)

Learning in Graphical Models (Adaptive Computation and Machine Learning)

[PDF.be96] Learning in Graphical Models (Adaptive Computation and Machine Learning)

Learning in Graphical Models From A Bradford Book epub
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 | #1661405 in Books |  1998-11-27 | Ingredients: Example Ingredients | Original language:English | PDF # 1 |  10.00 x1.25 x7.00l,2.45 | File type: PDF | 648 pages

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||33 of 37 people found the following review helpful.| Recommended, but not the place to begin|By Todd Ebert|The title of the book is somewhat misleading, in that most of the research papers involve advanced issues concerning one particular graphical model, namely the Bayesian network. For this reason I highly recommend, as a prerequisite to this book, Finn Jensen's "Bayesian Networks and Decision Graphs". Jensen's book is adequa| ||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 o

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 t...

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