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.
So, I must admit. This book is nothing like what I expected when I ordered it. Having a background in engineering, many statistics classes (some graduate level), and experience with other types of "networks," I was expecting a fairly tough book filled with theorems, equations, and trivial first-principle examples.
Was I ever wrong. Instead, this book discusses Bruce Willis blowing up a meteor, game shows, the author sleeping too long and missing work, and scenarios in Agatha Christie novels. In my stuffy statistics classes, Bayesian statistics was explained as something not short of gnosticism - that if you didn't get it already, it was not worth explaining. I used Bayes' Theorem for simple problems, but for nothing very practical - practical implementation was too difficult.
It is a sign that someone knows a complex subject very well: Explain it simply enough (and still be correct) so that a bright grade-school child would understand. This is exactly that the authors have done. The appealing and simple approach to this book hides the fact that they to an amazingly good job at explaining Bayesian statistics. The light subjects I mentioned in the paragraph above should be taken that these authors do a tremendous job at explaining themselves, not that this book is a pushover, or is simple or pedestrian.
If you, like me, have always wished that someone would have taken the time to explain Bayesian statistics to you, or if you are somewhat experienced in statistics but feel like you need more (or see the holes that these authors point out), then this book is for you. If you are looking for a book filled with proofs and theorems, or if you like authors that make themselves sound smart by intimidation and shock-and-awe, then skip this book. This book is the real deal.
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
No comments:
Post a Comment