Although many Bayesian Network (BN) applications are now in everyday use, BNs have not yet achieved mainstream penetration. Focusing on practical real-world problem solving and model building, as opposed to algorithms and theory, Risk Assessment and Decision Analysis with Bayesian Networks explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide powerful insights and better decision making.
Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, and more Introduces all necessary mathematics, probability, and statistics as needed The book first establishes the basics of probability, risk, and building and using BN models, then goes into the detailed applications. The underlying BN algorithms appear in appendices rather than the main text since there is no need to understand them to build and use BN models. Keeping the body of the text free of intimidating mathematics, the book provides pragmatic advice about model building to ensure models are built efficiently.
A dedicated website, www.BayesianRisk.com, contains executable versions of all of the models described, exercises and worked solutions for all chapters, PowerPoint slides, numerous other resources, and a free downloadable copy of the AgenaRisk software.
What I liked most about this book is how it starts off with giving a general introduction into probabilistic and causal reasoning, and then goes on into more detail on probability and, eventually, more complex mathematics, thus making it a perfect choice for both beginners and scholars of probability.
This is the textbook, which is suitable to everyone who really wants to see the Bayesian networks for the most useful tools they are. It does not just throw some abstract ideas at you, but rather builds on a series of real life examples, and provides an opportunity to actually try building some Bayesian models yourself, guiding the reader through the process.
Adding to that, the idea that this book comes with a very powerful software tool makes the offer even more appealing.
The authors explain in simple, readily accessible terms both the basics of probability theory and Bayesian approaches with an emphasis on Bayesian Networks. I wish there were more exercises, but that does not seem to be the purpose of this book - so no demerits. If you are looking for a next step, hands on Bayesian tutorial try "Doing Bayesian Analysis: A Tutorial with R and Bugs". This latter books assumes a bit of programming chops, but not much.
I have long been a fan of AgenaRisk for Bayesian Belief Networks, but didn't know what I didn't know about the theory and applications.
This new book opened my eyes, and also provided the opportunity for others to get hands-on experience with the scaled-down version of AgenaRisk.
I have been actively and enthusiastically recommending this book in Medtronic - and those who have purchased and started reading the book are echoing my enthusiasm and recommendation to others.
The book is well written, with accessible, informative, and often humorous examples that stimulate thought and understanding while keeping the reader grounded in the uses for the methodology.
Highly recommended!
Product Details :
- Hardcover: 524 pages
- Publisher: CRC Press; 1 edition (November 7, 2012)
- Language: English
- ISBN-10: 1439809100
- ISBN-13: 978-1439809105
- Product Dimensions: 1.1 x 6.9 x 9.8 inches
More Details about Risk Assessment and Decision Analysis with Bayesian Networks, 1st Edition
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