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1 of 1 people found the following review helpful
5.0 out of 5 stars A masterpiece, 15 Aug 2012
This review is from: Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series) (Hardcover)
This is the most complete book so far I've read on the topic with excellent description of the algorithms and models and a thorough presentation of proof. It's orientated toward Probabilistic Graphical Models only (as the title says), which means you cannot expect to read too much about other aspect of the classical Bayesian theory (I advise Christian Robert's book: A Bayesian choice, for that purpose).

The book is graduate level and needs the reader to have solid skills in linear algebra, probabilities and statistics to make the most of it.

What I really like about this book is the fact it only focus on one topic: Graphical models and do not try to cover Machine Learning in general. The consequence of that is its thorough treatment of many aspects of graphical models which is rare in the literature. That's why I highly and warmly recommend this book.
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0 of 1 people found the following review helpful
4.0 out of 5 stars Bayes network, Graph models, 27 Nov 2011
This review is from: Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series) (Hardcover)
This is a good reference book on probabilistic graph algorithms. The book is well written and covers computation and machine learning subjects to medium depth level. Good value for the money spent
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