Publications: Bayesian networks (general)



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


Fenton, N., & Neil, M. (2021). Calculating the Likelihood Ratio for Multiple Pieces of Evidence.


Constantinou AC, Fenton N and Neil M (2021), “How Do Some Bayesian Network Machine Learned Graphs Compare to Causal Knowledge?”,


Wang, H., Neil, M., & Fenton, N. E. (2021). A Recursive Method for Approximate Inference in Discrete Dynamic Bayesian Networks using Interface Junction Trees. IEEE Trans Pattern Anal Mach Intell, submitted.

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.

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. Accepted version (pdf). Blog post here.

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, Accepted version available here

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, (This is open access).

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)

Fenton N.E. (2018), "Handling Uncertain Priors in Basic Bayesian Reasoning", July 2018,

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.

Constantinou, A.,  Fenton N.E., "Things to know about Bayesian networks", Significance, 15(2), 19-23 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 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,, pdf preprint version available here

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

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,  Open access pre-publication version.   See blog posting.

Fenton NE, Neil M, Lagnado D, Marsh W, Yet B, Constantinou A (2016), "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   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

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.  pre-publication version here.  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,   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   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.,  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.

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,

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:
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.

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). 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.  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, 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 .  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, 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  (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

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 (see here for details of model)

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

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

Fenton, N.E. and Neil, M., '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.

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

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,


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, 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

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


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


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


Neil M, Fenton NE, Nielsen L, ''Building large-scale Bayesian Networks'', The Knowledge Engineering Review, 15(3), 257-284, 2000.





Fenton et al, “The SERENE Method Manual EC Project No. 22187 SERENE (SafEty and Risk Evaluation using bayesian Nets), SERENE/5.3/CSR/3053/R/1, 1999