Learning in Graphical Models (Adaptive Computation and Machine Learning) (Paperback)

by Michael I. Jordan (Editor) "The field of Bayesian networks, and graphical models in general, has grown enormously over the last few years, with theoretical and computational developments in many..." (more)
Key Phrases: multiinformation function, ordered overrelaxation, regular inference rule, Monte Carlo, Morgan Kaufmann, New York (more...)

Editorial Reviews

Review


"The state of the art presented by the experts in the field."
—Ross D. Shachter, Department of Engineering-Economic Systems and Operations Research, Stanford University

Product Description
"The state of the art presented by the experts in the field." -- Ross D. Shachter, Department of Engineering-Economic Systems and Operations Research, Stanford University

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 as the glue whereby the parts are combined, ensuring that the system as a whole is consistent and providing ways to interface models to data. Graph theory provides both an intuitively appealing interface by which humans can model highly interacting sets of variables and a data structure that lends itself naturally to the design of efficient general-purpose algorithms.

This book presents an in-depth exploration of issues related to learning within the graphical model formalism. Four chapters are tutorial chapters--Robert Cowell on Inference for Bayesian Networks, David MacKay on Monte Carlo Methods, Michael I. Jordan et al. on Variational Methods, and David Heckerman on Learning with Bayesian Networks. The remaining chapters cover a wide range of topics of current research interest.

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Product Details

• Paperback: 648 pages
• Publisher: The MIT Press; 1st edition (November 27, 1998)
• Language: English
• ISBN-10: 0262600323
• ISBN-13: 978-0262600323
• Product Dimensions: 10 x 6.9 x 1.4 inches
• Shipping Weight: 1.8 pounds (View shipping rates and policies)
• Average Customer Review: 4.5 out of 5 stars See all reviews (2 customer reviews)
• Amazon.com Sales Rank: #209,717 in Books (See Bestsellers in Books)

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  #21 in	Books > Science > Mathematics > Applied > Graph Theory
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  #29 in	Books > Computers & Internet > Computer Science > Artificial Intelligence > Machine Learning

Customer Reviews

Most Helpful Customer Reviews

19 of 22 people found the following review helpful:

4.0 out of 5 stars Recommended, but not the place to begin, June 18, 2003

By	Todd Ebert (Long Beach California) - See all my reviews

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.

11 of 25 people found the following review helpful:

5.0 out of 5 stars Simply Superb..., March 30, 2000

By A Customer

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!