Customer Reviews

Learning in Graphical Models (Adaptive Computation and Machine Learning)

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4 star:		(1)
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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 adequate in giving a good introduction and overview of the subject, but not sufficient for calling oneself an "expert" upon successfully digesting it.

To its credit, "Learning in Graphical Models" has several well-written and interesting papers, but the tutorial papers just did not seem enough of an introduction for me to feel comfortable using it as a first source of introduction.

What I find most compelling about Bayesian networks is the fact that they seem both highly modular (which facilitates reusability and network interconnectivity) and can be designed in a semi-rational manner (contrast this with neural-network architectures for which few good algorithms exist for determining size and number of layers). For this reason I imagine they will be important players in future engineering projects that require learning and adaptation.

The book contains a nice collection of papers relevant to Graphical models and machine learning. The book came in good condition and organization of topics is great!!! Some of the compiled papers are available online for free. However, I found the book to be useful because of its organization, logical flow and compilation of very relevant and useful papers related to the field.

My area of research revolves around graphical models... Best Book... The book that introduced me as to how effective graphical models are... As stated in the editorial review, graphical model is the marriage between graph theory and probability and this book justifies the sacredness of this marriage!