Just Published Smart data not big data Since

Just Published ‘Smart data’ – not ‘big data’ Since the first edition, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes extensive new material and applications including from finance, medicine, transport, law, forensics, and cybersecurity. Focusing on practical real-world problemsolving 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. www. crcpress. com

PROFESSOR NORMAN FENTON Turing Fellow Norman is a Professor at Queen Mary, University of London where he is Head of the Risk Assessment and Decision Analysis Research Group. He is joint founder and Director of Agena. Risk. PROFESSOR MARTIN NEIL Turing Fellow Martin is a Professor in Computer Science and Statistics at Queen Mary, University of London. He is joint founder and Director of Agena. Risk.

"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)

Model causal knowledge, uncertainty and data

Highlights • Provides all tools necessary to build and run realistic causal models (Bayesian networks) for decision and risk assessment • Supplies extensive example models based on real risk assessment problems in a wide range of application domains: finance, safety, systems reliability, law, forensics, cybersecurity and more • Introduces all necessary maths, probability, and statistics as needed • Supported by a dedicated website contains exercises and worked solutions for all chapters along with numerous other resources. • The Agena. Risk 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.

Automatically ‘solve’ the model for the optimal decision strategy

“Like the 1 st edition – it is a recommended first read; it’s well written, updated with additional material and topics that consider some contemporary issues. It’s vertically and horizontally broad in that it develops a good understanding of theory with application examples……More importantly, for serious practitioners having the software and examples combine to make this a good read but excellent learning tool. I have used this professionally for financial analysis, product performance assessment, and business decision making…The book is well worth it – when the 3 rd edition is published – I’ll buy that one too. ” Amazon Review, 8 Nov 2018

“So, I must admit. This book is nothing like what I expected when I ordered it. 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. ” Amazon Review by Serac

"The single most important book on Bayesian methods for decision analysts" Doug Hubbard (Best selling author in decision sciences)

“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. The appealing and simple approach to this book hides the fact that they do an amazingly good job at explaining Bayesian statistics. …. 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. …. This book is the real deal. ” Amazon Review by Serac

"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)

“This book was fantastic! It had everything I wanted to know about understanding Bayesian networks in addition to tangible examples to make learning the material easy. I read this cover to cover twice and felt that it really laid out the material in a way that built on previous chapters. Also it contains an excellent appendix to help understand some of the algorithms used by Agena. Risk as well as a basic introduction to combinatorics. I would high recommend this book. ” Sean Schaefer

“I have long been a fan of Agena. Risk for Bayesian Networks, but didn't know what I didn't know about 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 Agena. Risk. 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!” Eric Maas