Tuesday, 4 September 2018

It's finally arrived...

Still waiting to get our own copies of the second edition of the book, but one of our PhD students just received his copy, so it is real! The first edition (published Dec 2012) now has 437 Google scholar citations, and many dozens of 5-star reviews on Amazon.

Friday, 10 August 2018

Second Edition to be published 28 August 2018

From the back cover of the Second Edition:
"The single most important book on Bayesian methods for decision analysts" —Doug Hubbard (author in decision sciences and actuarial science)  
"The book provides sufficient motivation and examples (as well as the mathematics and probability where needed from scratch) to enable readers to understand the core principles and power of Bayesian networks." —Judea Pearl (Turing award winner)  
"The lovely thing about Risk Assessment and Decision Analysis with Bayesian Networks is that it holds your hand while it guides you through this maze of statistical fallacies, p-values, randomness and subjectivity, eventually explaining how Bayesian networks work and how they can help to avoid mistakes.” —Angela Saini (award-winning science journalist, author & broadcaster)
Since the first edition of this book published, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes new material on influence diagrams, learning from data, value of information, cybersecurity, debunking bad statistics, and much more. Focusing on practical real-world problem-solving and model building, as opposed to algorithms and theory, it explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide more powerful insights and better decision making than is possible from purely data-driven solutions.

  • 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, forensics, cybersecurity and more
  • Introduces all necessary mathematics, probability, and statistics as needed
  • Establishes the basics of probability, risk, and building and using Bayesian network models, before going into the detailed applications
A dedicated website contains exercises and worked solutions for all chapters along with numerous other resources. The AgenaRisk software contains a model library with executable versions of all of the models in the book. Lecture slides are freely available to accredited academic teachers adopting the book on their course.

Sample Chapters