NF
Norman Fenton
Queen Mary University of London, City University
Research areas: Computer Science · Information Systems · Bayesian Modeling and Causal Inference · Software Engineering Techniques and Practices · Software Engineering Research
0000-0003-2924-0510
Employment
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Queen Mary University of London Professor of Risk and Information Management2000 - Present
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City University1989 - 2000
Education
Education history is unavailable.
Projects & Funding
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CAUSAL-DYNAMICS (“Improved Understanding of Causal Models in Dynamic Decision-making”) Leverhulme Trust2017 - 2020
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PAMBAYESIAN: PAtient Managed decision-support using Bayesian networks Engineering and Physical Sciences Research Council2017 - 2020
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Effective Bayesian Modelling with Knowledge before Data European Research Council2014 - 2018
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DIADEM: Data Information and Analysis for clinical DEcision Making Engineering and Physical Sciences Research Council2008 - 2009
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DyFusion - Towards a Novel Universal tool for Modelling and Reasoning under Uncertainty Engineering and Physical Sciences Research Council2007 - 2010
Publications (199)
- Stacking Factorizing Partitioned Expressions in Hybrid Bayesian Network Models Save
- Influence of Seasonality and Public-Health Interventions on the COVID-19 Pandemic in Northern Europe Save
- Bayesian Network Modelling for the Clinical Diagnosis of Alzheimer’s Disease Save
- A hybrid Bayesian network for medical device risk assessment and management Save
- Region‐based estimation of the partition functions for hybrid Bayesian network models Save
- Statistical Issues in Serial Killer Nurse Cases Save
- mHealth apps for gestational diabetes mellitus that provide clinical decision support or artificial intelligence: A scoping review Save
- Bayesian Hypothesis Testing and Hierarchical Modeling of Ivermectin Effectiveness Save
- Bayesian networks in healthcare: What is preventing their adoption? Save
- Current review and next steps for artificial intelligence in multiple sclerosis risk research Save
- A Study of Using Bethe/Kikuchi Approximation for Learning Directed Graphic Models Save
- Dependencies in evidential reports: The case for informational advantages Save
- Product risk assessment: a Bayesian network approach Save
- On the limitations of probabilistic claims about the probative value of mixed DNA profile evidence Save
- Bayesian network analysis of Covid-19 data reveals higher infection prevalence rates and lower fatality rates than widely reported Save
- A privacy-preserving Bayesian network model for personalised COVID19 risk assessment and contact tracing Save
- A Note on UK Covid19 death rates by religion: which groups are most at risk? Save
- Managing Knowledge in Computational Models for Global Food, Nutrition and Health Technologies Save
- Bayesian Network Analysis of Covid-19 data reveals higher Infection Prevalence Rates and lower Fatality Rates than widely reported Save
- A note on 'Collider bias undermines our understanding of COVID-19 disease risk and severity' and how causal Bayesian networks both expose and resolve the problem Save