Full publications list
(chronological)

Books

Fenton, N.E. and M. Neil, Fighting Goliath: Exposing the flawed science and statistics behind the COVID-19 event.  2024, ‎Sovereign Rights Publishing, ISBN: ‎ 1068749830,  2024

Fenton, N.E. and M. Neil, Risk Assessment and Decision Analysis with Bayesian Networks, Second Edition.  2018, Chapman and Hall/CRC Press, ISBN: 9781138035119,  2018
Fenton, N. E. and J. Bieman (2014)  “Software Metrics: A Rigorous and Practical  Approach” (3rd Edition). CRC Press, ISBN 9781439838228
Fenton, N.E. and M. Neil, Risk Assessment and Decision Analysis with Bayesian Networks.  2012, CRC Press, ISBN: 9781439809105 , ISBN 10: 1439809100,  2012

Fenton NE and Pfleeger SL, 'Software Metrics: A Rigorous and Practical Approach', PWS ISBN (0534-95429-1), 1998 (originally published by International Thomson Computer Press, 1996)   

Fenton NE, Iizuka Y, and Whitty RW (Editors), 'Software Quality Assurance and Metrics: A Worldwide Perspective', International Thomson Computer Press, 1995.

Fenton NE and Hill G, Systems Construction and Analysis: A Mathematical and Logical Approach, McGraw Hill, 1992.

Fenton NE and Von Mayrhauser A (Editors), Proceedings of 1st Intl Software Metrics Symposium, IEEE Computer Society Press, 1993.

Fenton NE, 'Software Metrics: A Rigorous Approach', Chapman and Hall, 1991.

Fenton NE and Littlewood B (Eds), 'Software Reliability and Metrics', Elsevier, 1991.

 

Patents

Fenton NE and Neil M, Improved Programme Selection, International Patent Publication Number WO 03/090466 A2, World Intellectual Property Organisation International Bureau, 2003.

 

Articles

2026

O’Flynn C, Wright H, O’Rourke A, Harding A, Williams T, Wallis C, Harvey C, Constantaras M and Fenton N (2026) Risk assessment for canine periodontal disease using a hybrid causal Bayesian network. Front. Vet. Sci. 13:1781228. doi: 10.3389/fvets.2026.1781228

 

2025

McLachlan S, Lagnado D, Neil M, and Fenton N E, (2025) "Understanding the Evidence and Verdict in the Letby Trial Can the Innocent Really be Found Guilty of Serial Murder?" http://dx.doi.org/10.13140/RG.2.2.35256.25602

Gerry A. Quinn, Ronan Connolly, Norman Fenton et al, (2025) "What lessons can be learned from the management of the COVID-19 pandemic?", The International Journal of Public Health (IJPH): 70:1607727. doi: 10.3389/ijph.2025.1607727

​Lin, P., Neil, M., & Fenton, N. (2025). Stacking Factorizing Partitioned Expressions in Hybrid Bayesian Network Models, ACM Transactions on Knowledge Discovery from Data  19 (3), 1-28, https://doi.org/10.1145/3714473

​McLachlan, Scott, Bridget J. Daley, Sam Saidi, Evangelia Kyrimi, Kudawashe Dube, Crina Grosan, Martin Neil, Louise Rose, and Norman E. Fenton. 2025. “A Bayesian Network Model of Pregnancy Outcomes for England and Wales.” Computers in Biology and Medicine 189 (May): 110026. https://doi.org/10.1016/J.COMPBIOMED.2025.110026.

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2024

Quinn G A, Connolly M, Fenton N E, Hatfill S J, Hynds P, ÓhAiseadha C, Sikora K, Soon W, Connolly  R(2024), "Influence of Seasonality and Public-Health Interventions on the COVID-19 Pandemic in Northern Europe", Journal of Clinical Medicine, J. Clin. Med. 2024, 13(2), 334; https://doi.org/10.3390/jcm13020334

​Bate St Cliere, A and Fenton N.E. (2024) "Bayesian Network Modelling for the Clinical Diagnosis of Alzheimer’s disease"   https://www.medrxiv.org/content/10.1101/2023.12.30.23300452v1 
Online tool to access model via questionnaire interface: https://ad-diagnostic-tool.public.agenaai.app/

Hunte, J. L., Neil, M. & Fenton, N. E. (2024), "A hybrid Bayesian network for medical device risk assessment and management". Reliab. Eng. Syst. Saf. 241, 109630 https://doi.org/10.1016/j.ress.2023.109630

McLachlan S and Fenton N E, (2024), "How unusual was the spike in neonatal deaths when Lucy Letby was working?" http://dx.doi.org/10.13140/RG.2.2.13777.54886

​Hunte, J L, Osman, M, Fenton, N E., Neil, N and Bechlivanidis C (2024) “The effect of risk communication on consumers’ risk perception, risk tolerance and utility of smart and non-smart home appliances”, Safety Science https://doi.org/10.17863/CAM.106197

Neil, M., McLachlan, S. and Fenton, N. (2024) ‘The extent and impact of vaccine status miscategorisation on covid-19 vaccine efficacy studies’. medRxiv, https://doi.org/10.1101/2024.03.09.24304015

McLachlan, S. Daley, B., Kyrimi, E., Dube, K., Saidi, S., Grosan, C., Neil, M., Fenton, N., Rose, L. (2024). Approach and method for Bayesian Network Modelling: The case for pregnancy outcomes in England and Wales. 17th International Conference on Health Informatics (HEALTHINF). Rome, Italy. February 2024. https://doi.org/10.5220/0012428600003657

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2023

P. Chaichanavichkij, M. Hartmann,S. M. Scott, N. Fenton, C. H. Knowles (2023),  "Evaluating the risk factors for the development of benign disorders of defaecation: a surgical perspective", Techniques in Coloproctology, https://doi.org/10.1007/s10151-023-02843-w

 McLachlan S, Daley B J, Dube K, Kyrimi E, Neil M, Fenton N E, (2023) "The Health Condition Timeline as a Model for Pregnancy Disease Management", https://doi.org/10.1101/2023.02.06.23285418

McLachlan S, Dube K, Choi Y, Said S, Rose L, and Fenton N E, (2023) "A Structure, Understanding, Recent Developments and New Definition for Consent, http://dx.doi.org/10.13140/RG.2.2.34271.12961

Dewitt S H, Adler N, Li C, Stoilova E, Fenton N E, Lagnado D A (2023) "Categorical Updating in a Bayesian Propensity Problem", Cognitive Science 47 (7) e13313, https://doi.org/10.1111/cogs.13313

McLachlan S, Neil M, Choi Y, Craig S, Dube K, Engler J, Osman M, Fenton N E, (2023)  "Extended: Analysis of COVID-19 Vaccine Death Reports from the Vaccine Adverse Events Reporting System (VAERS) Database", http://dx.doi.org/10.13140/RG.2.2.23499.87842

Fenton NE, Neil M (2023), "The Lancet and the Pfizer Vaccine: A Case Study in Academic Censorship and Deceit in the Covid Era", http://dx.doi.org/10.13140/RG.2.2.29792.56321

 MacBrayne A, Soyel H, Marsh W, Fenton N E, Pitzalis C,  Curzon P,  and Humby F (2023), "Attitudes to Technology supported Rheumatoid Arthritis care: Opportunities & Barriers for technology in RA - Key themes from Qualitative arm of Mixed-Methods Study", British Journal of Rheumatology 62 (Supplement_2) http://dx.doi.org/10.1093/rheumatology/kead104.174

​MacBrayne A, Soyel H, Marsh W, Fenton N E, Pitzalis C,  Curzon P,  and Humby F (2023), "Attitudes to technology-supported rheumatoid arthritis care, questionnaire study: opportunities for technology to improve RA care" British Journal of Rheumatology 62 (Supplement_2), http://dx.doi.org/10.1093/rheumatology/kead104.322

Fenton, N E  (2023) 'Scottish cardiac ambulance data shows worrying increase in incidents' UK Column, 3 Jan 2023, https://www.ukcolumn.org/article/scottish-cardiac-ambulance-data-shows-worrying-increase-in-incidents

Fenton N E (2023) "The Prosecutor’s Fallacy and the IPCC Report",The Global Warming Policy Foundation, https://www.thegwpf.org/content/uploads/2023/09/Fenton-Bayes.pdf

 

2022 

Gill, R. D., Fenton, N., & Lagnado, D. (2022). Statistical Issues in Serial Killer Nurse Cases. Laws 2022, Vol. 11, Page 65, 11(5), 65. https://doi.org/10.3390/LAWS11050065  full pdf https://www.mdpi.com/2075-471X/11/5/65/pdf

Fenton, N. E., Neil, M., Craig, C. & McLachlan, S. (2022). "What the ONS Mortality Covid-19 Surveillance Data can tell us about Vaccine Safety and Efficacy", http://dx.doi.org/10.13140/RG.2.2.30898.07362

​Fenton, N. E., (2022) The Bangladesh Mask study: a Bayesian perspective, http://dx.doi.org/10.13140/RG.2.2.26189.92649

Craig, C., Neil, M., Fenton, N., McLachlan, S., Smalley, J., Guetzkow, J., Engler, J.,Russell, D., Rose, J. (2022). Official mortality data for England reveal systematic undercounting of deaths occurring within first two weeks of Covid-19 vaccination. https://doi.org/http://dx.doi.org/10.13140/RG.2.2.12472.42248

McLachlan, S., Dube, K., Schafer, B., Gillespie, A., Fenton, N. (2022) The Chaotic State of UK Drone Regulation. http://dx.doi.org/10.13140/RG.2.2.25709.87521

 McLachlan, S., Kyrimi, E., Dube, K., Fenton, N., & Webley, L. (2022). Lawmaps: Enabling Legal AI development through Visualisation of the Implicit Structure of Legislation and Lawyerly Process.  Artificial Intelligence and Law, 1–26. https://doi.org/10.1007/s10506-021-09298-0

McLachlan S, Neil M, Dube K, Bogani R, Fenton N E, and Schaffer B (2022), "Smart Automotive Technology Adherence to the Law: (De)Constructing Road Rules for Autonomous System Development, Verification and Safety", International Journal of Law and Information Technology,  https://doi.org/10.1093/ijlit/eaac002

Hartmann, M., Fenton, N E, Dobson, R. (2022). Development of Bayesian Network for Multiple Sclerosis Risk Factor Interaction Analysis. In: , et al. Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2021. Lecture Notes in Computer Science, vol 13483. Springer, Cham. https://doi.org/10.1007/978-3-031-20837-9_2

​Lin, P., Neil, M., Fenton, N., & Dementiev, E. (2022). Region‐based estimation of the partition functions for hybrid Bayesian network models. International Journal of Intelligent Systems. 37 (11), pp 8897-8927,  https://doi.org/10.1002/int.22973

 Hunte, J., Neil, M., & Fenton, N. E. (2022). "A causal Bayesian network approach for consumer product safety and risk assessment"  Journal of Safety Research 80, pp 198-214, https://doi.org/10.1016/j.jsr.2021.12.003

Chaichanavichkij, P., Hartmann, M., McLachlan, S., Scott, S., Fenton, N. E, & Knowles, C. (2022). O039 Evaluating the influence of risk factors on the development of defaecatory problems: a Delphi study. British Journal of Surgery, 109 (Supplement_4). https://doi.org/10.1093/bjs/znac242.039

 

2021

​Fenton, N. E., & Lagnado, D. A. (2021). Bayesianism: Objections and Rebuttals. In Christian Dahlman, A. Stein, & G. Tuzet (Eds.), Philosophical Foundations of Evidence Law, Chapter 18, pp 267-286. https://doi.org/10.1093/oso/9780198859307.003.0019   Preprint pdf is here.

 Lin, P., Neil, M., & Fenton, N. (2021). A Study of Using Bethe/Kikuchi Approximation for Learning Directed Graphic Models. IEEE Access, 9, 125428 - 125438. https://doi.org/10.1109/ACCESS.2021.3110956

Constantinou AC, Fenton N and Neil M (2021), “How Do Some Bayesian Network Machine Learned Graphs Compare to Causal Knowledge?”, http://arxiv.org/abs/2101.10461.

 

Fenton, N., & Neil, M. (2021). Calculating the Likelihood Ratio for Multiple Pieces of Evidence.  http://arxiv.org/abs/2106.05328

Hartmann M, Fenton NE and Dobson R, “Current Review and Next Steps for Artificial Intelligence in Multiple Sclerosis Risk Research” (2021)  Comput. Biol. Med. https://doi.org/10.1016/j.compbiomed.2021.104337  (also available here:  Pre-print pdf)

Neil, M, Fenton N E, Joel Smalley, Clare Craig, Joshua Guetzkow, Scott McLachlan, Jonathan Engler, Dan Russell and Jessica Rose (2021), “Official mortality data for England suggest systematic miscategorisation of vaccine status and uncertain effectiveness of Covid-19 vaccination”, http://dx.doi.org/10.13140/RG.2.2.28055.09124 (this is a significantly revised version of http://dx.doi.org/10.13140/RG.2.2.14176.20483)

​Neil, M, Fenton N E and Scott McLachlan (2021), "Discrepancies and inconsistencies in UK Government datasets compromise accuracy of mortality rate comparisons between vaccinated and unvaccinated", http://dx.doi.org/10.13140/RG.2.2.32817.10086  [A significantly updated version of the paper is here]

Fenton, N. E., Neil, M., & McLachlan, S. (2021). Paradoxes in the reporting of Covid19 vaccine effectiveness: Why current studies (for or against vaccination) cannot be trusted and what we can do about it,http://dx.doi.org/10.13140/RG.2.2.32655.30886

Neil, M and Fenton N E (2021), "Bayesian Hypothesis Testing and Hierarchical Modeling of Ivermectin Effectiveness", American Journal of Therapeutics, Vol 28, e577–e579, http://dx.doi.org/10.1097/MJT.0000000000001450

Neil, M and Fenton N E (2021), "Bayesian hypothesis testing and hierarchical modelling of ivermectin effectiveness in treating Covid-19" http://dx.doi.org/10.13140/RG.2.2.19703.75680 Updated version here

McLachlan,  S,  Osman, M,  Dube, K, Chiketero, P, Choi, Y, and Fenton N (2021) "Analysis of COVID-19 vaccine death reports from the Vaccine Adverse Events Reporting System (VAERS) Database",  http://dx.doi.org/10.13140/RG.2.2.26987.26402

​Fenton, N. E., Neil, M., & McLachlan, S. (2021). What proportion of people with COVID-19 do not get symptoms? https://doi.org/10.13140/RG.2.2.33939.60968

​Fenton N. E, Neil M, McLachlan S, Osman M (2021), "Misinterpreting statistical anomalies and risk assessment when analysing Covid-19 deaths by ethnicity", Significance, 18(2). https://www.significancemagazine.com/701  Pre-print: 10.13140/RG.2.2.18957.56807  Blog post here.

​Fenton, N. E., McLachlan, S., Lucas, P., Dube, K., Hitman, G., Osman, M., Kyrimi, E.  Neil, M. (2021). "A  Bayesian network model for personalised COVID19 risk assessment and contact tracing" https://doi.org/10.1101/2020.07.15.20154286

Daley B.J., Ni’Man M., Raniere Neves M., Huda M.S.B., Marsh W, Fenton N. E., Hitman G. A., and McLachlan S (2021) "mHealth Apps for Gestational Diabetes Mellitus that provide Clinical Decision Support or Artificial Intelligence: A Scoping Review", Diabetic Medicine http://dx.doi.org/10.1111/dme.14735

 

Hartmann M, Fenton NE and Dobson R (2021), "Using Bayesian networks to understand multiple sclerosis risk factor interactions", 37th Congress of the European Committee for Treatment and Research in Multiple Sclerosis, Oct 2021

​Kyrimi, E., McLachlan, S., Dube, K., Neves, M. R., Fahmi, A., & Fenton, N. (2021). A comprehensive scoping review of Bayesian networks in healthcare: Past, present and future. Artificial Intelligence in Medicine, 117, 102108. https://doi.org/10.1016/J.ARTMED.2021.102108

 ​Kyrimi E, Dube K, Fenton N E, Fahmi A, Raniere Neves M, Marsh W  & McLachlan, G. S. (2021), "Bayesian Networks in Healthcare: What is preventing their adoption?" Artificial Intelligence in Medicine, Vol 116, 102079,  https://doi.org/10.1016/j.artmed.2021.102079. PrePrint available here

Hartmann M, Fenton NE and Dobson R, “Current Review and Next Steps for Artificial Intelligence in Multiple Sclerosis Risk Research” (2021)  Comput. Biol. Med. https://doi.org/10.1016/j.compbiomed.2021.104337  (also available here:  Pre-print pdf)

​M. Hartmann, N. Fenton and R. Dobson. “A statistical analysis of multiple sclerosis risk factor interaction with Bayesian networks,” Computational Intelligence Methods for Bioinformatics and Biostatistics, 2021. https://weconf.eu/cibb-2021/presentation/a-statistical-analysis-of-multiple-sclerosis-risk-factor-interaction-with-bayesian-networks

​M. Hartmann, N. Fenton and R. Dobson. “Using Bayesian networks to understand multiple sclerosis risk factor interactions,” 37th Congress of the European Committee for Treatment and Research in Multiple Sclerosis, 2021 https://journals.sagepub.com/doi/full/10.1177/13524585211044667

  Hunte, J., Neil, M., & Fenton, N. E. (2021). A causal Bayesian network approach for consumer product safety and risk assessment: Research and Summary Report 2021/035. Office for Product Safety & Standards, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1018546/bayesian-networks-research-summary-report.pdf

 

2020

Fenton, N. E.  (2020) How to explain an increasing proportion of people testing positive for COVID if there is neither an increase in proportion of genuine cases nor increase in the false positive rate. https://doi.org/10.13140/RG.2.2.27902.20806

Fenton, N E., Neil M., & McLachlan, S. (2020). A Response to the Call for Evidence Regarding COVID-19 Data Transparency and Accountability  (UK Parliament, Public Administration and Constitutional Affairs Committee, Commons Select Committee). https://committees.parliament.uk/writtenevidence/13847/default/  Also available here.

​​Butcher, R., & Fenton, N. E. (2020). Extending the range of symptoms in a Bayesian Network for the Predictive Diagnosis of COVID-19, medRxiv https://doi.org/10.1101/2020.10.22.20217554

​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.

Prodhan, G., & Fenton, N. E. (2020). Extending the range of COVID-19 risk factors in a Bayesian network model for personalised risk assessment. medRxiv  https://doi.org/10.1101/2020.10.20.20215814

Fenton, N E. (2020). A Note on UK Covid19 death rates by religion: which groups are most at risk? http://arxiv.org/abs/2007.07083

Fenton, N. E., McLachlan, S., Lucas, P., Dube, K., Hitman, G., Osman, M., Kyrimi, E.,  Neil, M. (2020). "A privacy-preserving Bayesian network model for personalised COVID19 risk assessment and contact tracing". MedRxiv, 2020.07.15.20154286. https://doi.org/10.1101/2020.07.15.20154286

Fenton, N. E.  (2020) How to explain an increasing proportion of people testing positive for COVID if there is neither an increase in proportion of genuine cases nor increase in the false positive rate. https://doi.org/10.13140/RG.2.2.27902.20806

Collins, R., & Fenton, N. (2020). Bayesian network modelling for early diagnosis and prediction of Endometriosis. MedRxiv, 2020.11.04.20225946. https://doi.org/10.1101/2020.11.04.20225946

McLachlan S, Kyrimi E, Dube K, Hitman G, Simmonds J and Fenton N E, “Towards Standardisation of Evidence-Based Clinical Care Process Specifications” (2020) 26 Health Informatics J. 25 (4), 2512-2538,  https://doi.org/10.1177/1460458220906069

McLachlan, S., Kyrimi, E., Dube, K., Fenton, N., & Webley, L. (2020). Lawmaps: Enabling Legal AI development through Visualisation of the Implicit Structure of Legislation and Lawyerly Process.  http://arxiv.org/abs/2011.00586

Butcher, R., & Fenton, N. E. (2020). Extending the range of symptoms in a Bayesian Network for the Predictive Diagnosis of COVID-19, medRxiv https://doi.org/10.1101/2020.10.22.20217554

Prodhan, G., & Fenton, N. E. (2020). Extending the range of COVID-19 risk factors in a Bayesian network model for personalised risk assessment. 

 

 

medRxiv  https://doi.org/10.1101/2020.10.20.20215814

Hunte, J., Fenton, N. E., & Neil, M. (2020). Product risk assessment: a Bayesian network approach. https://arxiv.org/abs/2010.06698  

Lin, P., Neil, M., & Fenton, N.E.  (2020). Improved High Dimensional Discrete Bayesian Network Inference using Triplet Region Construction. Journal of Artificial Intelligence Research, 69, 231–295. https://doi.org/10.1613/jair.1.12198

Fenton, N.E. , Jamieson, A., Gomes, S., & Neil, M. (2020). "On the limitations of probabilistic claims about the probative value of mixed DNA profile evidence". http://arxiv.org/abs/2009.08850

Osman, M., McLachlan, S., Fenton, N. E., Neil, M., Löfstedt, R., & Meder, B. (2020). "Learning from behavioural changes that fail". Trends  in Cognitive Science,  https://doi.org/10.1016/j.tics.2020.09.009 Blog post here.  Accepted version (pdf).

Cruz, N., Hahn, U., Fenton, N. E., & Lagnado, D. A. (2020). Explaining away, augmentation, and the noisy assumption of independence. Frontiers in Psychology, 11, 502751. https://doi.org/10.3389/fpsyg.2020.502751 Accepted version (pdf). Blog post here.

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., 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. (2020). A Note on UK Covid19 death rates by religion: which groups are most at risk? http://arxiv.org/abs/2007.07083

Kyrimi, E., Neves, M., Neil, M., Marsh, W., McLachlan, S., & Fenton, N. E. (2020). "Medical idioms for clinical Bayesian network development". Journal of Biomedical Informatics, Vol 108, 103495,  https://doi.org/10.1016/j.jbi.2020.103495. Accepted version available here

Neil, M., Fenton, N E., Osman, M., & McLachlan, S. (2020). "Coronavirus: our study suggests more people have had it than previously estimated", The Conversation, 26 June 2020

Neil, M., Fenton, N.E, Osman, M., & McLachlan, S. (2020). "Bayesian Network Analysis of Covid-19 data reveals higher Infection Prevalence Rates and lower Fatality Rates than widely reported". Journal of Risk Research, 23 (7-8), 866-879 https://doi.org/10.1080/13669877.2020.1778771 . Preprint: MedRxiv, 2020.05.25.20112466. https://doi.org/10.1101/2020.05.25.20112466  Blog post here

Pilditch, T., Hahn, U., Fenton, N. E., & Lagnado, D. A. (2020). "Dependencies in evidential reports: The case for informational advantages". Cognition, Vol 204, 104343  https://doi.org/10.1016/j.cognition.2020.104343   Preprint (accepted version) here. Blog post here

Osman, M., Fenton, N. E. , McLachlan, S., Lucas, P., Dube, K., Hitman, G. A.,  Kyrimi, E, Neil, M, (2020)."The thorny problems of Covid-19 Contact Tracing Apps: The need for a holistic approach", Journal of Behavioral Economics for Policy, Vol. 4, 57-61.  Published version. Also available here.

Dewitt, S., Fenton, N. E., & Liefgreen, AliceLagnado, D. A. (2020). Propensities and second order uncertainty: a modified taxi cab problem. Frontiers in Psychology, 11, 503233. https://doi.org/10.3389/fpsyg.2020.503233  Accepted version (pdf). Blog post here.

McLachlan, S., Dube, K., Hitman, G. A., Fenton, N. E., & Kyrimi, E. (2020). Bayesian networks in healthcare: Distribution by medical condition. Artificial Intelligence in Medicine, 107, 101912.  https://doi.org/10.1016/J.ARTMED.2020.101912

Dube, K., Mclachlan, S., Zanamwe, N., Kyrimi, E., Thomson, J. S., & Fenton, N. E (2020.). "Managing Knowledge in Computational Models for Global Food, Nutrition and Health Technologies."  2020 IEEE Global Humanitarian Technology Conference (GHTC) (GHTC 2020)  https://doi.org/10.1109/GHTC46280.2020.9342880 

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

McLachlan, S., Lucas, P., Dube, K., McLachlan, G. S., Hitman, G. A., Osman, M., Kyrimi, E, Neil, M, Fenton, N. E. (2020). "The fundamental limitations of COVID-19 contact tracing methods and how to resolve them with a Bayesian network approach". https://doi.org/10.13140/RG.2.2.27042.66243

McLachlan, S., Lucas, P., Dube, K., Hitman, G. A., Osman, M., Kyrimi, E., … Fenton, N. E. (2020). Bluetooth Smartphone Apps: Are they the most private and effective solution for COVID-19 contact tracing?  http://arxiv.org/abs/2005.06621

Fenton, N. E. (2020). "The Deer Hunter: A lesson in the basics of risk and probability assessment". https://doi.org/10.13140/RG.2.2.31675.98089. (also available here). Blog post here and video

Fenton, N. E., Neil, M., Osman, M., & McLachlan, S. (2020). "COVID-19 infection and death rates: the need to incorporate causal explanations for the data and avoid bias in testing". Journal of Risk Research, 1–4. https://doi.org/10.1080/13669877.2020.1756381

Fenton, N. E., Neil, M., & Constantinou, A. (2020). The Book of Why: The New Science of Cause and Effect, Judea Pearl, Dana Mackenzie, Basic Books (2018). Artificial Intelligence, 284, 103286.  https://doi.org/10.1016/J.ARTINT.2020.103286

Fenton, N.E., Hitman, G. A., Neil, M., Osman, M., & McLachlan, S. (2020). Causal explanations, error rates, and human judgment biases missing from the COVID-19 narrative and statistics. PsyArXiv Preprints. https://doi.org/10.31234/OSF.IO/P39A4

Fenton, N. E., Osman, M., Neil, M., & McLachlan, S. (2020). Coronavirus: country comparisons are pointless unless we account for these biases in testing. The Conversation,  April 2, 2020  Spanish version: Coronavirus: las comparaciones entre países no tienen sentido a menos que tengamos en cuenta los sesgos en las pruebas.

Kyrimi, E,  McLachlan, S, Dube, K, Neves M R, Fahmi,A,  Fenton, N E, (2020) "A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Future", arXiv:2002.08627   

Kyrimi, E., McLachlan, S., Dube, K., & Fenton, N.E  (2020). Bayesian Networks in Healthcare: the chasm between research enthusiasm and clinical adoption. MedRxiv, 2020.06.04.20122911. https://doi.org/10.1101/2020.06.04.20122911

McLachlan S., Kyrimi E., Dube K., Fenton N. (2020) Standardising Clinical Caremaps: Model, Method and Graphical Notation for Caremap Specification. In: Roque A. et al. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2019. Communications in Computer and Information Science, vol 1211. Springer, Cham  https://doi.org/10.1007/978-3-030-46970-2_21

Daley, B. J., Kyrimi, E., Dube, K., Fenton, N. E., Hitman, G. A., & McLachlan, S. (2020). Data Visualisation in Midwifery: The Challenge of Seeing what Datasets Hide. Studies in Health Technology and Informatics, 270, 1239–1240. https://doi.org/10.3233/SHTI200381

McLachlan, S., Kyrimi, E., & Fenton, N. (2020). Public Authorities as Defendants: Using Bayesian Networks to determine the Likelihood of Success for Negligence claims in the wake of Oakden.  http://arxiv.org/abs/2002.05664

Wang, J., Neil, M., & Fenton, N. E. (2020). "A Bayesian Network Approach for Cybersecurity Risk Assessment Implementing and Extending the FAIR Model". Computers and Security, Vol 89. DOI: 10.1016/j.cose.2019.101659
See also blog post.

2019

Mclachlan, S., Dube, K., Kyrimi, E., & Fenton, N. (2019). "LAGOS: learning health systems and how they can integrate with patient care". BMJ Health Care Inform, 26, 100037. https://doi.org/10.1136/bmjhci-2019-100037

Dube, K., McLachlan, S., Zanamwe, N., Kyrimi, E., Thomson, J., & Fenton, N. (2019). "Managing Knowledge Incorporated into Solution Models for Customisable Global Health Technologies". IEEE Global Humanitarian Technology Conference (GHTC), ISBN: 978-1-7281-1780-5/19 pages 303-310.

Zhang, H., Marsh, W. R., Fenton, N., & NEIL, M. (2019). "Realising the Potential for ML from Electronic Health Records". Proc. 1st International ‘Alan Turing’ Conference on Decision Support and Recommender Systems (DSRS-Turing 2019). London, UK. Accepted version (pdf)

McLachlan, Scott, Kudakwashe Dube, Thomas Gallagher, Jennifer A. Simmonds, and Norman Fenton. 2019. “Realistic Synthetic Data Generation: The ATEN Framework.” In , 497–523. Springer, Cham. https://doi.org/10.1007/978-3-030-29196-9_25.

Fenton, N. E. (2019). Book Review: Pat Wiltshire’s “Traces: The memoirs of a forensic scientist and criminal investigator” 535 Books, 2019. https://doi.org/10.13140/RG.2.2.29938.66247 Also available here.

Fenton, N. E. (2019). Book Review: David Spiegelhalter’s “The Art of Statistics: How to Learn from Data.” London UK.  https://doi.org/10.13140/RG.2.2.28462.46400  Also available here

Daley, B., Hitman, G., Fenton, N.E., & McLachlan, S. (2019). "Assessment of the methodological quality of local clinical practice guidelines on the identification and management of gestational diabetes". BMJ Open, 9(6), e027285. https://doi.org/10.1136/bmjopen-2018-027285.  Full paper (pdf)

Noguchi, T., Fenton, N. E., & Neil, M. (2019). Addressing the Practical Limitations of Noisy-OR using Conditional Inter-causal Anti-Correlation with Ranked Nodes. IEEE Transactions on Knowledge and Data Engineering, 31(4): 813-817,  http://doi.org/10.1109/TKDE.2018.2873314. (This is open access).

Dewitt S.H., Hsu A.S., Lagnado D.A., Desai S.C,  Fenton N.E. (2019) "Nested Sets and Natural Frequencies",  COGSCI 2019,  41st Annual Meeting of the Cognitive Science Society, Montreal, Canada, July 24th – Saturday July 27th, 2019 . Accepted paper (pdf)

Fenton, N. E., Lagnado, D. A., Dahlman, C., & Neil, M. (2019). "The Opportunity Prior: A proof-based prior for criminal cases", Vol 18(4), 237-253 Law, Probability and Risk, DOI 10.1093/lpr/mgz007. Full paper from OUP.

Fenton, N. E.. (2019). When “absence of forensic evidence” is not “neutral.” https://doi.org/10.13140/RG.2.2.14517.73440

Fenton, N. E.., Neil, M., Yet, B., & Lagnado, D. A. (2019). "Analyzing the Simonshaven Case using Bayesian Networks". Topics in Cognitive Science, 10.1111/tops.12417 .  The published version can be read here: https://rdcu.be/bqYxp   See also blog post

Fenton, N. E. (2019)  "Hannah Fry’s 'Hello World' and the Example of Algorithm Bias", DOI 10.13140/RG.2.2.14339.55844  Download pdf  See also blog post

Stephen Dewitt, Adler, N., Fenton, N. E., & Lagnado, D. (2019). "Categorical Propensity Updating: A Novel Form of Confirmation Bias". Cogn Psychol, sumitted.

de Zoete, J., Fenton, N. E., Noguchi, T., & Lagnado, D. A. (2019). "Countering the ‘probabilistic paradoxes in legal reasoning’ with Bayesian networks". Science & Justice 59 (4), 367-379  10.1016/j.scijus.2019.03.003  The  pre-publication version (pdf)  The models   See also blog post.

McLachlan, S., Dube, K., Johnson, O., Buchanan, D., Potts, H. W. W., Gallagher, T., Marsh, D.W.,  Fenton, N. E. (2019). "A Framework for Analysing Learning Health Systems: Are we removing the most impactful barriers?", Learning Health Systems, March 2019, Vol 3 (4), e10189 10.1002/lrh2.10189.  

McLachlan, S., Kyrimi, E., Dube, K., & Fenton, N. E.. (2019). "Clinical Caremap Development: How can caremaps standardise care when they are not standardised?" In HealthInf 13 Annual International Conference on Health Informatcis. Prague, Czech Republic. Feb 2019 Pre-publication version (pdf)

Neil, M., Fenton, N. E., Lagnado, D. A. & Gill, R. (2019), "Modelling competing legal arguments using Bayesian Model Comparison and Averaging".  Artififical Intelligence and Law Vol 27, 403-430 . https://doi.org/10.1007/s10506-019-09250-3. The full published version can be read here. Pre-publication version (pdf)

Neil, M., Fenton, N. E., Osman, M., & Lagnado, D. A. (2019). Causality, the critical but often ignored component guiding us through a world of uncertainties in risk assessment. Journal of Risk Research, to 10.1080/13669877.2019.1606454.  Pre-publication version (pdf).

Fenton, N. E., Noguchi, T.  & Neil, M (2019). "An extension to the noisy-OR function to resolve the “explaining away” deficiency for practical Bayesian network problems". IEEE Transactions on Knowledge and Data Engineering, 31(12), 2441-2445 DOI: 10.1109/TKDE.2019.2891680   Accepted version (pdf)

Pilditch, T., Fenton, N. E., & Lagnado, D. A. (2019). "The zero-sum fallacy in evidence evaluation". Psychological Science Vol 30 (2),  pp 250-260  http://doi.org/10.1177/0956797618818484  See also blog posting.

 

2018

Fenton, N. E., (2018) "A Bayesian Network and Influence Diagram for a simple example of Drug Economics Decision Making", https://doi.org/10.13140/RG.2.2.33659.77600

Dewitt, S., Lagnado, D., & Fenton, N. E. (2018). "Updating Prior Beliefs Based on Ambiguous Evidence". In CogSci 2018 (pp. 306–311). Madison Wisconsin, 25-28 July 2018. ISBN: 978-0-9911967-8-4. see also Blog Post

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. (2018). On the Role of Statistics in Miscarriages of Justice. In 3rd Meeting of the All-Party Parliamentary Group on Miscarriages of Justice. House of Commons, London 25 June 2018. https://doi.org/10.13140/RG.2.2.22791.70567

Fenton N.E. & Neil, M. (2018). "How Bayesian Networks are pioneering
the ‘smart data’ revolution", Open Access Government, July 2018 pages 22-23. pdf version  Also available here.

Fenton N.E., & Neil, M. (2018). "Improving Software Testing with Causal Modelling". In R. Kennet, F. Ruggeri, & F. Faltin (Eds.), Analytic Methods in Systems and Software Testing (pp. 27–63). John Wiley & Sons Ltd. https://doi.org/10.1002/9781119357056.ch2

McLachlan, S., Potts, H., Dube, K., Buchanan, D., Lean, S., Gallagher, T., Johnson, O., Daley, B., Marsh, W., &  Fenton N.E. (2018), "The Heimdall Framework for Supporting Characterisation of Learning Health Systems",  BCS Journal of Innovation in Health Informatics, 25(2):77–87, http://dx.doi.org/10.14236/jhi.v25i2.996

Osman, M., Fenton N.E.., Pilditch, T., Lagnado, D. A., & Neil. M. (2018). "Who do we trust on social policy interventions". Basic and Applied Social Psychology, Vol 40 (5), 249-268  https://doi.org/10.1080/01973533.2018.1469986. Open access version pdf

McLachlan, S., Dube, K., Buchanan, D., Lean, S., Johnson, O., Potts, H., Gallagher, T,. Marsh, W.,  Fenton N.E. (2018). "Learning health systems: The research community awareness challenge". Journal of Innovation in Health Informatics, 25(1), 038-040 http://doi.org/10.14236/jhi.v25i1.981

Constantinou, A.,  Fenton N.E., (2018) "Things to know about Bayesian networks", Significance, 15(2), 19-23 https://doi.org/10.1111/j.1740-9713.2018.01126.x Full pdf also available here.

Fenton N.E. (2018) "Evidence based decision making turns knowledge into power", EU Research 'Beyond the Horizon' Magazine, Spring 2018, pp 38-39. PDF version here.

Yet, B., Constantinou, A.,  Fenton N.E.. & Neil, M. (2018) "Expected Value of Partial Perfect Information in Hybrid Models using Dynamic Discretization",  IEEE Access, Vol 6, pp 7802-7817 https://doi.org/10.1109/ACCESS.2018.2799527. Full pdf version also available here

Yet B, Neil M,  Fenton N.E., Dementiev E, Constantinou A. (2018), "An Improved Method for Solving Hybrid Influence Diagrams", International J Approx Reasoning, Vol 95, pp 93-112, https://doi.org/10.1016/j.ijar.2018.01.006, pdf preprint version available here

Fenton N.E.,  & Neil, M. (2018). Response to Nick Thieme's: "Statistic of the Year", not "Statistic of the Next Ten Years", 10.13140/RG.2.2.30958.72002

Fenton N.E.,  & Neil, M. (2018). "Lawnmowers versus terrorists: A highly misleading view of risk", Significance 15(1), 12-15. http://onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2018.01104.x/full Full pdf  also available here

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. .

Fenton N.E.,  & Neil, M. (2018). "Is decision-making using historical data alone more dangerous than lawnmowers?", Open Access Report here.  Also available here.

Fenton N.E,  & Neil, M. (2018). "Are lawnmowers a greater risk than terrorists?", Open Access Report here.  Also available here.

 

2017

Fenton N.E., Lagnado D, de Zoete, J, "Modeling complex legal cases as a Bayesian network (BN) using idioms and sensitivity analysis with the Collins case as a complete example",  ICFIS2017 (10th International Conference on Forensic Inference and Statistics), Mineapolis, USA,   Sept 2017. 10.13140/RG.2.2.35414.55360

de Zoete, J, Fenton N.E. ,"Automatic Generation of Bayesian networks in Forensic Science",  ICFIS2017 (10th International Conference on Forensic Inference and Statistics), Mineapolis, USA,   Sept 2017, 10.13140/RG.2.2.17798.47689

Constantinou, A., & Fenton, N.E (2017). "The future of the London Buy-To-Let property market: Simulation with Temporal Bayesian Networks".  PLoS ONE 12(6): e0179297  doi.org/10.1371/journal.pone.0179297  (open access) 27 June 2017

Balding, D., Fenton, N. E., Gill, R., Lagnado, D. & Schneps, L. "Twelve Guiding Principles and Recommendations for Dealing with Quantitative Evidence in Criminal Law". (2017). Isaac Newton Institute Report INI 16061, http://www.newton.ac.uk/files/preprints/ni16061.pdf


Neil, M. & Fenton, N.E.  "Risk Management Using Bayesian Networks" in Wiley StatsRef: Statistics Reference Online 1–6 (John Wiley & Sons, Ltd, 2017). doi:10.1002/9781118445112.stat07943


Fenton, N.E., Constantinou, A., & Neil, M. (2017). "Combining judgments with messy data to build Bayesian Network models for improved intelligence analysis and decision support". In Subjective Probability, Utility and Decision Making Conference (SPUDM 17). Haifa, Israel.

Fenton, N. E., Lagnado, D. A., Dahlman, C., & Neil, M. (2017). The Opportunity Prior: A Simple and Practical Solution to the Prior Probability Problem for Legal Cases. ICAIL '17 Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law, ACM, pp 69-76,  10.1145/3086512.3086519  Published by ACM. Pre-publication draft.

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.

2016

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
 

Dementiev E and Fenton N E, "Bayesian Torrent Classification by File Name and Size Only", International Conference on Probabilistic Graphical Models, Lugano, Switzerland, 06 Sep 2016 - 09 Sep 2016. Journal of Machine Learning Research. 52: 136-147. 09 Sep 2016.  Published version.

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

Zhou, Y., Fenton, N. E., Zhu, C. (2016), "An Empirical Study of Bayesian Network Parameter Learning with Monotonic Causality Constraints",  Decision Support Systems Vol 87, pages 69-79.  http://dx.doi.org/10.1016/j.dss.2016.05.001  pre-publication version here.  See also blog posting

Yet, B., Constantinou, A. C., Fenton, N., Neil, M., Luedeling, E., & Shepherd, K. (2016). A Bayesian Network Framework for Project Cost, Benefit and Risk Analysis with an Agricultural Development Case Study.  Expert Systems with Applications, Volume 60 Oct 2016, pages 141-155  http://dx.doi.org/10.1016/j.eswa.2016.05.005  pre-publication version here  See also blog posting

Fenton N.E, Neil M, Berger D, “Bayes and the Law”, Annual Review of Statistics and Its Application, Volume 3, 2016 (June), pp 51-77  http://dx.doi.org/10.1146/annurev-statistics-041715-033428 .Pre-publication version here and here is the Supplementary Material See also blog posting

Smit, N. M., Lagnado, D. A., Morgan, R. M., & Fenton, N. E. (2016). "Using Bayesian networks to guide the assessment of new evidence in an appeal case".  Crime Science, 2016, 5: 9, DOI 10.1186/s40163-016-0057-6 (open source). Published version pdf.  See also blog posting

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.

Zhou, Y., Hospedales, T., Fenton, N. E. (2016), "When and where to transfer for Bayes net parameter learning", Expert Systems with Applications. 55,  361-373 http://dx.doi.org/10.1016/j.eswa.2016.02.011.   See also blog posting

Constantinou, A. C., Fenton, N., Marsh, W., & Radlinski, L. (2016). "From complex questionnaire and interviewing data to intelligent Bayesian Network models for medical decision support", Artificial Intelligence in Medicine, 2016. Vol 67 pages 75-93.  http://dx.doi.org/10.1016/j.artmed.2016.01.002,  Pre-publication version here.

Constantinou, A. C., Yet, B., Fenton, N., Neil, M., & Marsh, W. (2016). Value of Information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences. Artificial Intelligence in Medicine.  66, pp 41-52 doi:10.1016/j.artmed.2015.09.002   Pre-publication version here.

2015

Constantinou, A.C., Yet, B., Fenton, N.E., Neil, M., Marsh, D.W.R., 2015. What is the value of missing information when assessing decisions that involve actions for intervention? . Atlas Sci. 2015

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. Associated model.

Fenton NE, Neil M (2015), "Book Review: Malcom Kendrick “Doctoring Data: How to sort out medical advice from medical nonsense”. Download. Also http://dx.doi.org/10.13140/RG.2.1.4904.8804

Shepherd, K., Hubbard, D., Fenton, N. E., Claxton, K., Luedeling, E., de Leeuw, J., (2015)  "Development goals should enable decision-making", Nature 532:  152-154,  9 July 2015, http://dx.doi.org/10.1038/523152a

Constantinou, A., Freestone M., Marsh, W., Fenton, N. E. , Coid, J. (2015) "Risk assessment and risk management of violent reoffending among prisoners",   Expert Systems With Applications 42 (21), 7511-7529. Pre-publication draft here.  Published version:  http://dx.doi.org/10.1016/j.eswa.2015.05.025
 
Zhou, Y., Fenton, N. E., Hospedales, T, & Neil, M. (2015). "Probabilistic Graphical Models Parameter Learning with Transferred Prior and Constraints", 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015), Amsterdam, 13-15 July 2015.

Yet, B., Constantinour A., Fenton N. E., Neil M., Leudeling E., Shepherd, K.,  "Project Cost, Benefit and Risk Analysis using Bayesian Networks",  Bayesian Applications Workshop, 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015), Amsterdam, 16 July 2015. Published as abstract.

Chockler, H., Fenton N.E., Koeppens J., Lagnado, D. (2015), "Causal Analysis for Attributing Responsibility in Legal Cases",  15th International Conference on Artificial Intelligence & Law  (ICAIL 2015), San Diego, June 8-12, 2015, pp 33-42, ACM ISBN 978-1-4503-3522-5. Open access version.

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

de Zoete, J, Sjerps, M, Lagnado,D,  Fenton, N.E. (2015), "Modelling crime linkage with Bayesian Networks" Law, Science & Justice, 55(3), 209-217. http://doi:10.1016/j.scijus.2014.11.005  Pre-publication draft here.  Slides from ICFIS 2014 Presentation

 

2014

Fenton, N. E. (2014). Assessing evidence and testing appropriate hypotheses. Science & Justice, 54(6), 502-504.  Pre-publication draft. Published version:  http://dx.doi.org/10.1016/j.scijus.2014.10.007   

Lin, P., Neil, M. & Fenton, N. E. Risk Aggregation in the presence of Discrete Causally Connected Random Variables. Ann. Actuar. Sci. 8, 298–31 (2014). http://dx.doi.org/10.1017/S1748499514000098. Pre-publication draft here.

Fenton, N. E., & Neil, M. (2014). "Decision Support Software for Probabilistic Risk Assessment Using Bayesian Networks". IEEE Software, 31(2), 21–26. http://dx.doi.org/10.1109/MS.2014.32  Author's final version here.

Fenton, N.E, Lagnado, D., Hsu, A., Berger, D., & Neil, M. (2014). Response to “on the use of the likelihood ratio for forensic evaluation: response to Fenton et al.”. Science & Justice : Journal of the Forensic Science Society, 54(4), 319–20. doi:10.1016/j.scijus.2014.05.005

Constantinou, A. C., Fenton, N. E., & Pollock, L. (2014). Bayesian networks for unbiased assessment of referee bias in Association Football. Psychology of Sport & Exercise, 15(5) 538–547,  http://dx.doi.org/10.1016/j.psychsport.2014.05.009. Pre-publication draft here.

Zhou, Y., Fenton, N. E., & Neil, M. (2014). An Extended MPL-C Model for Bayesian Network Parameter Learning with Exterior Constraints. In L. van der Gaag & A. J. Feelders (Eds.), Probabilistic Graphical Models: 7th European Workshop. PGM 2014, Utrecht. The Netherlands, September 17-19, 2014 (pp. 581–596). Springer Lecture Notes in AI 8754. Pre-publication draft here.

Fenton, N. E., Neil, M., & Hsu, A. (2014). "Calculating and understanding the value of any type of match evidence when there are potential testing errors". Artificial Intelligence and Law, 22. 1-28 . http://dx.doi.org/10.1007/s10506-013-9147-x  Pre-publication draft here. Note that Table 2 is wrong in the published version. See change.

Fenton, N. E.(2014)  "A Bayesian Network for a simple example of Drug Economics Decision Making", working paper DOI: http://10.13140/RG.2.1.1130.1281

Fenton, N. E., Neil, M. (2014) "Who put Bella in the wych elm?  A Bayesian analysis of a 70 year-old mystery", Technical Report produced for BBC Radio 4 Programme Punt-PI, 2 August 2014

Lin, P., Neil, M., & Fenton, N. E. (2014). "Risk Aggregation in the presence of Discrete Causally Connected Random Variables". Annals of Actuarial Science, 8(2), 298-319, http://dx.doi.org/10.1017/S1748499514000098. Pre-publication draft here.

Fenton, N. E., D. Berger, D. Lagnado,  M. Neil and A. Hsu, (2014). "When ‘neutral’ evidence still has probative value (with implications from the Barry George Case)",  Science and Justice, 54(4), 274-287 http://dx.doi.org/10.1016/j.scijus.2013.07.002  (pre-publication draft here)

Zhou, Y., Fenton, N., & Neil, M. (2014). Bayesian network approach to multinomial parameter learning using data and expert judgments. International Journal of Approximate Reasoning, 55(5), 1252-1268   http://dx.doi.org/10.1016/j.ijar.2014.02.008

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)

 

2013

Constantinou, Anthony C. & Fenton, N. E. (2013). Profiting from arbitrage and odds biases of the European football gambling market,  Journal of Gambling Business and Economics, Vol. 7(2), 41-70. Journal  link here.  Pre-publication draft here.

Constantinou, A., N. E. Fenton and M. Neil (2013) "Profiting from an Inefficient Association Football Gambling Market: Prediction, Risk and Uncertainty Using Bayesian Networks". Knowledge-Based Systems. Vol 50,  60-86 http://dx.doi.org/10.1016/j.knosys.2013.05.008

Fenton, N. E., D. Lagnado and M. Neil (2013). "A General Structure for Legal Arguments Using Bayesian Networks."   Cognitive Science 37, 61-102 http://dx.doi.org/10.1111/cogs.12004.  Pre-publication version here.

Constantinou, A. C. and N. E. Fenton (2013). "Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries." Journal of Quantitative Analysis in Sports 9(1): 37-50. Pre-publication version    http://dx.doi.org/10.1515/jqas-2012-0036

Lagnado, D. A., N. E. Fenton and M. Neil (2013). "Legal idioms: a framework for evidential reasoning." Argument and Computation, 2013,  4(1), 46-63 http://dx.doi.org/10.1080/19462166.2012.682656

Zhou, Y., Fenton, N. E., Neil, M., & Zhu, C. (2013). Incorporating Expert Judgement into Bayesian Network Machine Learning. In 23rd International Joint Conference on Artificial Intelligence (IJCAI2013) (pp. 3249–3250). China: AAAI Press. 

Yun Zhou, Norman Fenton, Martin Neil, Cheng Zhu, "Incorporating Expert Judgement into Bayesian Network Machine Learning", 23rd  International Joint Conference on Artificial Intelligence (IJCAI2013), 2013

 

2012

Fenton, N.E., Neil M, Lagnado, D, "Using soft evidence to model mutually exclusive causes in Bayesian networks", Technical Report, 2012

Constantinou, A., N. E. Fenton and M. Neil (2012). ""pi-football: A Bayesian network model for forecasting Association Football match outcomes." Knowledge Based Systems, 36, 322-339. Pre-publication version.   http://dx.doi.org/10.1016/j.knosys.2012.07.008

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
DOI: http://dx.doi.org/10.13140/RG.2.1.3030.6726

Neil, M, Chen X,  Fenton, N E, "Optimizing the Calculation of Conditional Probability Tables in Hybrid Bayesian Networks using Binary Factorization", IEEE Transactions on Knowledge and Data Engineering, 24(7), 1306 - 1312, 2012  http://dx.doi.org/10.1109/TKDE.2011.87

Fenton, N.E. and Neil, M.(2012), 'On limiting the use of Bayes in presenting forensic evidence',  Extended draft available here.

Constantinou, A. , Fenton, N.E., "Solving the problem of inadequate scoring rules  for assessing probabilistic football forecasting models", Journal of Quantitative Analysis in Sports, Vol. 8 (1), Article 1, 2012.  http://dx.doi.org/10.1515/1559-0410.1418  Preprint draft here.

 

2011

Fenton, N. E. (2011). "Science and law: Improve statistics in court." Nature 479: 36-37.  Paper on Nature online website is here. http://dx.doi.org/10.1038/479036a  An extended draft on which this was based is here. 

Fenton, N.E. and Neil, M. (2011), 'Avoiding Legal Fallacies in Practice Using Bayesian Networks', Australian Journal of Legal Philosophy 36, 114-151, 2011 ISSN 1440-4982 (extended preprint draft here).

Fenton, N.E. and Neil, M. (2011), 'The use of Bayes and causal modelling in decision making, uncertainty and risk', UPGRADE, the Journal of CEPIS (Council of European Professional Informatics Societies), 12(5), 10-21, 2011. Published verion here.

Yet, B., Perkins Z.,Marsh, W., Fenton, N.E., "Towards a Method of Building Causal Bayesian Networks for Prognostic Decision Support",  ProBioMed 11, Bled, Slovenia, July 2011

Fenton, N. E. (2011). "Rational software developers as pathological code hackers" in The Dark Side of Software Engineering: Evil on Computing Projects. (Eds Rost, J. and Glass, R. L.), IEEE Computer Society Press, ISBN: 978-0-470-59717-0, pp 264-268

2010

Fenton, N. and Neil, M. (2010). "Comparing risks of alternative medical diagnosis using Bayesian arguments." Journal of Biomedical Informatics, 43: 485-495,  http://dx.doi.org/10.1016/j.jbi.2010.02.004
Preprint here.

Xiangjun, Li and  Fenton, N. E. "Applying Extended Support Vector Machines  to Discover Temporal Periodic Patterns",  Second  Global Congress on Intelligent Systems (GCIS 2010), Wuhan, China 2010.

Neil, M., Marquez, D. and Fenton, N. E. (2010). "Improved Reliability Modeling using Bayesian Networks and Dynamic Discretization." Reliability Engineering & System Safety, 95(4), 412-425, http://dx.doi.org/10.1016/j.ress.2009.11.012

 

2009

Fenton, N. E., Hearty, P., Neil, M. and Radliński, Ł. (2009). "Software Project and Quality Modelling Using Bayesian Networks Artificial Intelligence" in Applications for Improved Software Engineering Development: New Prospects. (Eds Meziane, F. and Vadera, S. Hershey), New York, USA, IGI Global: Chapter 1,1-25.

Fineman, M., Radlinski, L. and Fenton, N. E. (2009). Modelling Project Trade-off Using Bayesian Networks. IEEE Int. Conf. Computational Intelligence and Software Engineering. Wuhan, China, IEEE Computer Society. http://dx.doi.org/10.1109/CISE.2009.5364789 

Fineman, M. and Fenton, N. E. (2009). Quantifying Risks Using Bayesian Networks. IASTED Int. Conf. Advances in Management Science and Risk Assessment (MSI 2009). Beijing, China, IASTED. 662-219, pp 1227-1233

Radliński, Ł. & Fenton, N., 2009. Causal Risk Framework for Software Projects. In Z. Wilimowska et al. Information Systems Architecture and Technology. IT Technologies in Knowledge Oriented Management Process. Wrocław, Poland: Oficyna Wydawnicza Politechniki Wrocławskiej, pp. 49-59.

Hearty, P., Fenton, N., Marquez, D., and Neil, M., Predicting Project Velocity in XP using a Learning Dynamic Bayesian Network Model. IEEE Trans Software Eng, 2009. 35(1): 124-137.
doi.ieeecomputersociety.org/10.1109/TSE.2008.76

Fenton, N. E. (2009). Position Statement on the Role and Future of Search Based Software Engineering. 1st International Symposium on Search Based Software Engineering. Windsor, UK, IEEE Computer Society: xxii-xxiii.

 

2008

Radliński Ł , Fenton N E, Neil M, Zarządzaniu II w, "A Learning Bayesian Net for Predicting Number of Software Defects Found in a Sequence of Testing",
Polish Journal of Environmental Studies 17 (3B), 359-364, 2008

Fenton, N.E. and Neil, M., Avoiding Legal Fallacies in Practice Using Bayesian Networks (Seventh International Conference on Forensic Inference and Statistics. 2008: Lausanne, Switzerland).

Fenton, N.E., Neil, M., and Marquez, D., Using Bayesian Networks to Predict Software Defects and Reliability. Proceedings of the Institution of Mechanical Engineers, Part O, Journal of Risk and Reliability, 2008. 222(O4): p. 701-712, 10.1243/1748006XJRR161

Fenton, N.E., Neil, M., Marsh, W., Hearty, P., Radlinski, L., and Krause, P., On the effectiveness of early life cycle defect prediction with Bayesian Nets. Empirical Software Engineering, 2008. 13: p. 499-537.
10.1007/s10664-008-9072-x

Marquez, D., Neil, M., and Fenton, N., Solving Dynamic Fault Trees using a New Hybrid Bayesian Network Inference Algorithm, in 16th Mediterranean Conference on Control and Automation (MOD 08). 2008: Ajaccio, Corsica, France, pp 609-614,  http://dx.doi.org/10.1109/MED.2008.4602222

Marquez, D., Neil, M., and Fenton, N.E., Reliability Modelling Using Hybrid Bayesian Networks, in ISBIS-2008 International Symposium on Business and Industrial Statistics. 2008: Prague, Czech Republic.
http://dx.doi.org/10.1016/j.ress.2007.03.009

Neil, M., Marquez, D., and Fenton, N., Using Bayesian Networks to Model the Operational Risk to Information Technology Infrastructure in Financial Institutions. Journal of Financial Transformation, 2008. 22: p. 131-138.

Neil, M., Tailor, M., Marquez, D., Fenton, N.E., and Hearty, P., Modelling dependable systems using hybrid Bayesian networks. Reliability Engineering and System Safety, 2008. 93(7): p. 933-939.
http://dx.doi.org/10.1016/j.ress.2007.03.009

2007

Radliński, Ł., Fenton, N.E., Neil, M., and Marquez, D., Improved Decision-Making for Software Managers Using Bayesian Networks, in 11th IASTED Int. Conf. Software Engineering and Applications (SEA). 2007: Cambridge, MA, USA p. 13–19.

Fenton, N.E. and Neil, M., Managing Risk in the Modern World: Bayesian Networks and the Applications, 1. 2007, London Mathematical Society, Knowledge Transfer Report. 

Marquez D, Neil M, Fenton NE, "Improved Dynamic Fault Tree modelling using Bayesian Networks", The 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2007, Edinburgh 2007

Fenton NE, Neil M, Marquez D, "Using Bayesian Networks to Predict Software Defects and Reliability", 5th International Mathematical Methods in Reliability Conference (MMR 07), Glasgow 1-4 July 2007

Radliński, Ł., Fenton, N.E., Neil, M., and Marquez, D., Modelling Prior Productivity and Defect Rates in a Causal Model for Software Project Risk Assessment. Polish Journal of Environmental Studies, 2007. 16(4A): p. 256-260 

Marquez D, Neil M, Fenton NE, "A new Bayesian Network approach to Reliability modelling", 5th International Mathematical Methods in Reliability Conference (MMR 07), Glasgow 1-4 July 2007

Fenton NE, Neil M,  Marsh W, Hearty P, Krause P, Radliński Ł. , "Project Data Incorporating Qualitative Factors for Improved Software Defect Prediction, ICSE PROMISE 2007   The dataset and model associated with this paper can be found here.

Fenton NE, Neil M, and Caballero JG, "Using Ranked nodes to model qualitative judgements in Bayesian Networks" IEEE TKDE 19(10), 1420-1432, Oct 2007

Neil, M., Fenton, N., and Marquez, D., Using Bayesian Networks and Simulation for Data Fusion and Risk Analysis, in NATO Science for Peace and Security Series: Information and Communication Security, Skanata and Byrd, D.M., Editors. 2007, IOS Press, Nieuwe Hemweg 6B, 1013 BG Amsterdam, The Netherlands

Radliński, Ł., Fenton, N.E., Marquez, D., and Hearty, P., Empirical Analysis of Software Defect Types, in Information Systems Architecture and Technology: Information Technology and Web Engineering: Models, Concepts & Challenges (Proceedings of 28 International ISAT Conference). 2007, Oficyna Wydawnicza Politechniki Wrocławskiej, Wrocław: Szklarska, Poreba, Poland. p. 223-231

Khodakerami V, Fenton NE, Neil M, "Project Scheduling: Improved approach to incorporating uncertainty using Bayesian Networks", Project Management Journal, Jun 2007, Vol. 38 Issue 2, p39-49, 2007

Fenton NE, Neil M, Hearty P, Marsh W, Marquez D, Krause P, Mishra R, "Predicting Software Defects in Varying Development Lifecycles using Bayesian Nets", Information & Software Technology, Vol 49, pp 32-43, Jan 2007

 

2006

Fenton NE and Neil M, "Expert Elicitation for Reliable System Design",  Statistical Science, 2006 21(4), 451-453

Norman Fenton, Łukasz Radliński, Martin Neil "Improved Bayesian Networks for Software Project Risk Assessment Using Dynamic Discretisation,  IFIP Conference Software Engineering Techniques (SET 2006), Warsaw, Poland,  17-20 Oct 2006, in "Software Engineering Techniques: Design for Quality ", pp 139-148, Springer Boston, ISBN 978-0-387-39387-2,  http://dx.doi.org/10.1007/978-0-387-39388-9_14

Fenton NE and Wang W  , "Risk and Confidence Analysis for Fuzzy Multicriteria Decision Making", Knowledge Based Systems Vol 19, 430-437, 2006

Joseph A, Fenton NE, Neil M, "Predicting football results using Bayesian Nets and other Machine Learning Techniques", Knowledge Based Systems, Volume 19, Issue 7, Pages 544-553, Nov 2006
Old Version with additional data is here.

Neil M,  Marquez D, Fenton N, Tailor M, Hearty P, "Modelling Dependable Systems using Hybrid Bayesian Networks", First International Conference on Availability, Reliability and Security (ARES  2006), 20-22 April 2006, Vienna, Austria

2005

Neil M,  Fenton N, Tailor M, "Using Bayesian Networks to model Expected and Unexpected Operational Losses", Risk Analysis: An International Journal, Vol 25(4), 963-972, 2005

Hearty P, Fenton NE, Neil M, Cates P,  "Automated population of causal models for improved software risk assessment", 20th IEEE/ACM International Conference on Automated Software Engineering, Long Beach, California, USA, November 7-11, 2005, pp 433-435, ACM Press, ISBN: 1-59593-993-4

Neil M,  Fenton N, "Improved Methods for building large-scale Bayesian Networks", The Third Bayesian Modeling Applications Workshop, Uncertainty in Artificial Intelligence (UAI) 2005, Edinburgh University, 26 July, 2005

Neil M,  Fenton N, “Improved Software Defect Prediction”,  10th European SEPG, London, 2005

Fenton NE and Neil M, ''A Critique of Software Defect Prediction Models'',  in Machine Learning Applications in Software Engineering (eds: Zhang D, Tsai JJP), pp 72-86, ISBN 981-256-094-7, World Scientific Publishing Co, 2005

 

2004

Fenton NE and Neil M, "Combining evidence in risk analysis using Bayesian Networks", Safety Critical Systems Club Newsletter 13 (4), pp 8-13 Sept 2004

Fenton NE, Marsh W, Neil M, Cates P, Forey S, Tailor T, "Making Resource Decisions for Software Projects", 26th International Conference on Software Engineering (ICSE 2004), May 2004, Edinburgh, United Kingdom. IEEE Computer Society 2004, ISBN 0-7695-2163-0, pp. 397-406

 

2003

 

Neil M, Krause P, Fenton NE, "Software Quality Prediction Using Bayesian Networks" in Software Engineering with Computational Intelligence,  (Ed Khoshgoftaar TM), Kluwer,  ISBN 1-4020-7427-1, Chapter 6, 2003

Neil M, Fenton N, Forey S and Harris R. "Assessing Vehicle Reliability using Bayesian Networks" in Global Vehicle Reliability, Edited by J. E. Strutt and P.L. Hall. Professional Engineering Publishing, 25-42, 2003.

 

2002

Fenton N, Krause P, Neil M, "Probabilistic Modelling for Software Quality Control", Journal of Applied Non-Classical Logics 12(2), 173-188, 2002

Fenton NE, Krause P, Neil M, "Software Measurement: Uncertainty and Causal Modelling",  IEEE Software 10(4), 116-122, 2002

 

2001

Fenton NE, "Conducting and Presenting Empirical Software Engineering",  Journal of Empirical Software Engineering 6(3), 195-200, 2001

Fenton NE and Neil M, ''Making Decisions: Using Bayesian Nets and MCDA'', Knowledge-Based Systems 14, 307-325, 2001.

Neil M, Fenton N, Forey S and Harris R, "Using Bayesian Belief Networks to Predict the Reliability of Military Vehicles", IEE Computing and Control Engineering J  12(1), 11-20, 2001 

Fenton N, Krause P, Neil M, "Software Metrics: Uncertainty and Causal Modelling",. EuroSPI conference, Limerick Institute of Technology, Limerick, 10th-12th October 2001.

Fenton N, Krause P, Neil M, "Probabilistic Modelling for Software Quality Control", Sixth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty September 19-21, 2001, Toulouse, France.

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