Publications: Probability, Statistics and Smart data
Fenton N. E, Neil M, McLachlan S, Osman M (2020), "Misinterpreting statistical anomalies and risk assessment when analysing Covid-19 deaths by ethnicity", 10.13140/RG.2.2.18957.56807 Also here: preprint. Blog post here. To appear in Significance.
Fenton, N E (2020), "Why most studies into COVID19 risk factors may be producing flawed conclusions-and how to fix the problem", http://arxiv.org/abs/2005.08608 Blog post here
Fenton, N E., Neil, M., & Frazier, S. (2020). The role of collider bias in understanding statistics on racially biased policing. http://arxiv.org/abs/2007.08406
Fenton N.E. (2018), "Handling Uncertain Priors in Basic Bayesian Reasoning", July 2018, https://doi.org/10.13140/RG.2.2.16066.89280
Fenton N.E., & Neil, M. (2018). "Criminally Incompetent Academic Misinterpretation of Criminal Data - and how the Media Pushed the Fake News", Open Access Report 10.13140/RG.2.2.32052.55680
Constantinou, A. C. and Fenton, N.E. (2017). Towards Smart-Data: Improving predictive accuracy in long-term football team performance. Knowledge-Based Systems, Vol 124, pages 93-104, http://dx.doi.org/10.1016/j.knosys.2017.03.005 Open access pre-publication version. See blog posting.
Fenton NE, Neil M, Lagnado D, Marsh W, Yet B, Constantinou A, "How to model mutually exclusive events based on independent causal pathways in Bayesian network models", Knowledge-Based Systems, Dec 2016 Vol 113, pages 39-50. Gold access full version http://dx.doi.org/10.1016/j.knosys.2016.09.012 See also blog posting
Constantinou A and Fenton NE. "Improving predictive accuracy using Smart-Data rather than Big-Data: A case study of soccer teams' evolving performance" In Proceedings of the 13th UAI Bayesian Modeling Applications Workshop (BMAW 2016), 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), New York City, USA, June 25-29, 2016. Published version
Constantinou, A. C., Fenton, N.E, & Neil, M. (2016). Integrating expert knowledge with data in causal probabilistic networks: preserving the data-driven expectations when the expert variables remain unobserved. Expert Systems with Apllications, 56 pp 197-208, http://dx.doi.org/10.1016/j.eswa.2016.02.050. Pre-publication version.
Fenton, N.E., 2015. Debunking report that claims gender diverse executive Boards outperform male-only Boards, Queen Mary University of London, Report Number BK_TR_05_15, http://dx.doi.org/10.13140/RG.2.1.1221.4160/1
Fenton NE, Neil M, Constantinou A (2015) "Simpson’s Paradox and the implications for medical trials". Working paper. Associared model.
Fenton, N. E, "Another machine learning fable", March 2015 DOI: http://dx.doi.org/10.13140/RG.2.1.2506.3849
Fenton, N. E, "Moving from big data and machine learning to smart data and causal modelling: a simple example from consumer research and marketing", March 2015. DOI: http://dx.doi.org/10.13140/RG.2.1.3292.8166
Yet, B., Perkins Z., Fenton, N.E., Tai, N., Marsh, W., (2014) "Not Just Data: A Method for Improving Prediction with Knowledge", Journal of Biomedical Informatics, Vol 48, 28-37 http://dx.doi.org/10.1016/j.jbi.2013.10.012 (see here for details of model)
Fenton NE, "A simple story illustrating why pure machine learning (without expert input) may be doomed to fail and totally unnecessary", 12 Nov 2012 http://www.eecs.qmul.ac.uk/~norman/papers/ml_simple_example.pdf