Modeling and Reasoning with Bayesian Networks Hardcover – 6 Apr 2009
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'… both practical and advanced … The first five chapters are sufficient for students and practitioners to gain the necessary knowledge in order to build Bayesian networks for moderately sized applications with the aid of a software tool … All major inference methods are covered in later chapters which allow researchers and software developers to implement their own software systems tailored to their needs … It is a comprehensive book that can be used for self study by students and newcomers to the field or as a companion for courses on probabilistic reasoning. Experienced researchers may also find deeper information on some topics. In my opinion, the book should definitely be [on] the bookshelf of everyone who teaches Bayesian networks and builds probabilistic reasoning agents.' Artificial Intelligence
'[This] book will make an excellent textbook; it covers topics suitable for both undergraduate and graduate courses. It will also help practitioners get a firm grasp of the fundamentals of modeling and inference with BNs, as well as some recent advances.' ACM Computing Reviews
This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis.See all Product Description
Most Helpful Customer Reviews on Amazon.com (beta)
The book begins with the fundamentals of logic. It continues on to describe the properties of the Bayesian network graph such as independence relationships and d-separation as well as how the parameters of a Bayesian network work.
There are then in depth discussions of the various queries we are able to perform on Bayesian networks and the algorithms for accomplishing them. These include queries such as probability of evidence, most probable explanation and probabilistic inference. Techniques such as summing out, pearl's polytree algorithm and belief propagation are described elogently and clearly.
The book also contains information on the current state of the art research going on in the field. This book is a valuable resource for anyone new to or ingrained in the use of Bayesian Networks. A book of this scope and target was sorely needed and I for one am glad it has arrived. I would and have recommended this to any of my peers in the field.
This is an excellent text, with very clear explanations and step by step descriptions in pseudo code of the important algorithms in the text.
The first few chapters lay the probabilistic foundations needed for understanding Bayesian Networks and the conditional independences such networks encode.
Chapter 5 gives examples in several different domains of using Bayesian Networks to model different systems and answer queries about them.
After this, the book gets into the meat of its primary focus, efficient probabilistic inference in the context of Bayesian Networks.
It lays out various algorithms for exact inference using jointrees or recursive conditioning, and the complexity and trade-offs of the different approaches.
It further details further refinements that can reduce networks in some cases for even better performance.
After this, it details approximate inference techniques including sampling and belief propagation.
Chapter 14 on belief propagation is especially good, with its discussions on the semantics of belief propagation, generalized belief propagation, and an alternative formulation of generalized belief propagation edge deletion belief propagation.
The last few chapters also delve into learning Bayesian Networks structure and parameters.
All in all, this book will give an in depth knowledge of exact and approximate inference in Bayesian networks and a good overview of learning and applying these models to various domains.
My background and current focus: About 6 months ago at my company, I found the need to formalize reasoning regarding derived system requirements. As I struggled with how to reason and create a logically consistent set of verifiable, probabilistic requirements, I remembered Bayesian Networks, which, a few years ago, Prof. Paul Cohen (UofAz) mentioned to me. My recall sparked an investigation, starting with Kevin Murphy's BNT Matlab Toolbox and his excellent survey of tools, which is how I found Darwiche & SamIam (we also use BNT, but we find teaching BNs easier with SamIam's GUI).
My intuition is that, thanks to this book, expanded application of Bayesian networks will revolutionize the way that we design & develop algorithms and systems at my company.
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