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Learning Bayesian Networks

4.8 out of 5 stars 5 customer reviews
ISBN-13: 978-0130125347
ISBN-10: 0130125342
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Product Details

  • Hardcover: 674 pages
  • Publisher: Pearson (April 6, 2003)
  • Language: English
  • ISBN-10: 0130125342
  • ISBN-13: 978-0130125347
  • Product Dimensions: 7 x 1.6 x 8.9 inches
  • Shipping Weight: 2.5 pounds (View shipping rates and policies)
  • Average Customer Review: 4.8 out of 5 stars  See all reviews (5 customer reviews)
  • Amazon Best Sellers Rank: #1,015,253 in Books (See Top 100 in Books)

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Customer Reviews

Top Customer Reviews

By Dr. Lee D. Carlson HALL OF FAME on May 16, 2004
Format: Hardcover Verified Purchase
In just a decade, Bayesian networks have went from being a mere academic curiosity to a highly useful field with myriads of applications. Indeed, the applications of Bayesian networks are wide-ranging and include disparate fields such as network engineering, bioinformatics, medical diagnostics, and intelligent troubleshooting. This book gives a fine overview of the subject, and after reading it one will have an in-depth understanding of both the underlying foundations and the algorithms involved in using Bayesian networks. The reader will have to look elsewhere for applications of Bayesian networks, since they are only discussed briefly in the book. Due to space constraints, only the first four chapters will be reviewed here.
The author defines a Bayesian network as a graphical structure for representing the probabilistic relationship among a large number of variables and for performing probabilistic inference with these variables. Before the advent of Bayesian networks, probabilistic inference depended on the use of Bayes' theorem, which entailed that the problems examined be relatively simple, due to the exponential space and time complexity that can arise in the application of this theorem.
After a short review of probability theory in chapter 1, a discussion of the "philosophical" foundations of probability, and a discussion of the difficulties inherent in representing large instances and in performing inference over a large number of variables, the author introduces Bayesian networks as directed acyclic graphs satisfying the Markov condition. A brief discussion of NasoNet, which is a large-scale Bayesian network used in the diagnosis and prognosis of nasopharyngeal cancer, is given.
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Format: Hardcover
Rarely do I find myself reading a technical book
so carefully as this one. I always enjoy
books on Bayesian inference,
but this is the first that shows me how
to write useful algorithms. I appreciate
the level of mathematical rigor, too, for
such a new subject. Bayesian networks are what
neural networks should be, without the ad-hoc
theory and trial-and-error algorithms.
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Format: Hardcover
Neapolitan makes an attempt to give an instructive overview of Bayesian networks. Be prepared for an advanced graduate level reading and for encountering some beauty.

An absolute prerequisite is knowledge of college level math. In fact mathematics is the bones of this treatise. And also you should have a good understanding of algorithms - the flesh of this book. It is very helpful to have some previous knowledge on Bayesian statistics and even on Bayesian networks. The corresponding section in the excellent Artificial Intelligence: A Modern Approach (2nd Edition) (Prentice Hall Series in Artificial Intelligence) is probably not enough.

If you are a mathematician you might sometimes be bewildered due to a somewhat loose notation, due to a deep motivation in the algorithmic application of inferring probabilities from evolving knowledge of actual data and due to sometimes a strange usage of theorems. An example is theorem 3.1 (in preparation for Pearl's message passing algorithm) which is more a summary of its "proof" than anything else.

A rare treasure found in "Learning Bayesian Networks" is the delicate treatment of the philosophical issues.
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Format: Hardcover
Probably the best book to start with Bayesian Networks. This book is for both, pros and newbies.
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Format: Hardcover Verified Purchase
If you have no idea of BN then this is where to start, if your work involves BN reasoning
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