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Norman Fenton is Professor of Risk Information Management at Queen Mary London University and is also a Director of Agena, a company that specialises in risk management for critical systems. Norman is a mathematician by training whose current research focuses on critical decision-making and, in particular, on quantifying uncertainty. This typically involves analysing and predicting the probabilities of unknown events using causal, probabilistic models (Bayesian networks). This type of reasoning enables improved assessment by taking account of both statistical data where available and also expert judgment, providing more powerful insights and better decision making than is possible from purely data-driven solutions. Hence, the approach can be summarized as 'smart data rather than big data'. Applications include law and forensics (Norman has been an expert witness in major criminal and civil cases), medicine, security, software reliability, transport safety and reliability, finance, and football prediction. Norman also has a special interest in raising public awareness of the importance of probability theory and Bayesian reasoning in everyday life (including how to present such reasoning in simple lay terms) and he maintains a website dedicated to this and also a blog focusing on probability and the law.
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论文共 190 篇作者统计合作学者相似作者
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Scott McLachlan, Bridget J. Daley, Sam Saidi,Evangelia Kyrimi,Kudakwashe Dube, Crina Grossan,Martin Neil, Louise Rose,Norman E. Fenton
International Joint Conference on Biomedical Engineering Systems and Technologiespp.604-612, (2024)
Amber-Rose Bate St Cliere,Norman Fenton
medrxiv(2024)
RELIABILITY ENGINEERING & SYSTEM SAFETY (2024): 109630-109630
Gerry A. Quinn, Michael Connolly,Norman E. Fenton, Steven J. Hatfill,Paul Hynds,Coilin Ohaiseadha, Karol Sikora, Willie Soon, Ronan Connolly
JOURNAL OF CLINICAL MEDICINEno. 2 (2024): 334
Scott McLachlan,Bridget Daley, Sam Saidi,Evangelia Kyrimi,Kudakwashe Dube, Crina Grossan,Martin Neil,Louise Rose,Norman Fenton
medrxiv(2024)
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMSno. 11 (2022): 8897-8927
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