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Risk Assessment And Decision Analysis With Bayesian Networks 2nd Edition.
Publisher:
Taylor & Francis India
| Author:
Norman Fenton | Martin Neil
| Language:
English
| Format:
Hardback
Publisher:
Taylor & Francis India
Author:
Norman Fenton | Martin Neil
Language:
English
Format:
Hardback
₹3,820
Save: 15%
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Ships within:
3-5 Days
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ISBN:
Page Extent:
660
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.
Features
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.
Description
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.
Features
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.
About Author
Norman Fenton is Professor of Risk Information Management in the School of Electronic Engineering and Computer Science at Queen Mary University of London and is also a Director of Agena, a company that specialises in risk management for critical systems. Norman is a mathematician by training who now works on quantitative risk assessment. His experience covers a wide range of application domains such as legal reasoning (he has been an expert witness in major criminal and civil cases), medical analytics, vehicle reliability, embedded software, transport systems, financial services, and football prediction. Norman has a special interest in raising public awareness of the importance of probability theory and Bayesian reasoning in everyday life. Norman has published 7 books and 250 referred articles.
Martin Neil is a Professor in Computer Science and Statistics in the School of Electronic Engineering and Computer Science at Queen Mary, University of London and is also a Director and joint founder and of Agena Ltd, who develop and distribute AgenaRisk, a software product for modeling risk and uncertainty. In addition to working on theoretical and algorithmic foundations, his research covers a wide range of application domains including medical analytics, legal reasoning, embedded software, operational risk in finance, systems and design reliability (including software), project risk, commercial risk, decision support, cost benefit analysis, AI and personalization, machine learning, legal argumentation and cyber security. At Queen Mary he teaches decision and risk analysis. Martin was a fellow at the Newton Institute for Mathematical Sciences, Cambridge University in 2016 and was invited to the Fields Institute for Research in Mathematical Sciences, University of Toronto, Canada in 2010. Martin has published over 100 refereed articles.
Martin Neil is a Professor in Computer Science and Statistics in the School of Electronic Engineering and Computer Science at Queen Mary, University of London and is also a Director and joint founder and of Agena Ltd, who develop and distribute AgenaRisk, a software product for modeling risk and uncertainty. In addition to working on theoretical and algorithmic foundations, his research covers a wide range of application domains including medical analytics, legal reasoning, embedded software, operational risk in finance, systems and design reliability (including software), project risk, commercial risk, decision support, cost benefit analysis, AI and personalization, machine learning, legal argumentation and cyber security. At Queen Mary he teaches decision and risk analysis. Martin was a fellow at the Newton Institute for Mathematical Sciences, Cambridge University in 2016 and was invited to the Fields Institute for Research in Mathematical Sciences, University of Toronto, Canada in 2010. Martin has published over 100 refereed articles.
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