Research

Bayesian Artificial Intelligence, uncertainty quantification for optimal real-world decision making, Intelligent Games and Game Intelligence

Interests

His research interests are in Bayesian Artificial Intelligence and uncertainty quantification for optimal real-world decision making. He applies his research to a wide range of areas including sports, economics, medicine and forensics, for both academic research and industrial organisations. He currently collaborates with organisations world-wide primarily in, but not limited to, the sports betting industry.

http://www.constantinou.info

Publications

Publications of specific relevance to Applied Data Science

2021

Constantinou AC, Liu Y, Chobtham K, Guo Z and Kitson NK (2021). Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data. Elsevier  International Journal of Approximate Reasoning  vol. abs/2005.09020, 10.1016/j.ijar.2021.01.001

2020

Kitson NK and Constantinou AC (2020). Learning Bayesian networks from demographic and health survey data. Elsevier  Journal of Biomedical Informatics  10.1016/j.jbi.2020.103588

Guo Z and Constantinou AC (2020). Approximate learning of high dimensional Bayesian network structures via pruning of Candidate Parent Sets. Mdpi Ag  Entropy  vol. 22, (10) 10.3390/e22101142

Chobtham K and Constantinou AC (2020). Bayesian network structure learning with causal effects in the presence of latent variables. In Proceedings of The 10th International Conference On Probabilistic Graphical Models (Pgm-2020), Aalborg, Denmark 

Constantinou A (2020). The importance of temporal information in Bayesian network structure learning. Elsevier  Expert Systems With Applications  vol. 164, 10.1016/j.eswa.2020.113814

Constantinou AC (2020). Learning Bayesian Networks That Enable Full Propagation of Evidence. Ieee Access  vol. 8, 124845-124856. 10.1109/access.2020.3006472

Fenton N, Neil M and Constantinou A (2020). The Book of Why: The New Science of Cause and Effect, Judea Pearl, Dana Mackenzie, Basic Books (2018). Elsevier Bv  Artificial Intelligence  vol. 284, 103286-103286. 10.1016/j.artint.2020.103286

Constantinou A (2020). Learning Bayesian Networks with the Saiyan algorithm. Association For Computing Machinery  Acm Transactions On Knowledge Discovery From Data  10.1145/3385655

Constantinou AC (2020). Asian Handicap football betting with Rating-based Hybrid Bayesian Networks. Corr  vol. abs/2003.09384,

Constantinou AC (2020). Learning Bayesian Networks with the Saiyan Algorithm. Acm Trans. Knowl. Discov. Data  vol. 14, 44:1-44:1.

Constantinou AC (2020). Learning Bayesian Networks That Enable Full Propagation of Evidence. Ieee Access  vol. 8, 124845-124856.

2019

Constantinou AC (2019). Evaluating structure learning algorithms with a balanced scoring function. Corr  vol. abs/1905.12666,

Fenton NE, Neil M and Constantinou AC (2019). Simpson's Paradox and the implications for medical trials. Corr  vol. abs/1912.01422,

2018

CONSTANTINOU AC (2018). Dolores: A model that predicts football match outcomes from all over the world. Springer Verlag  Machine Learning  10.1007/s10994-018-5703-7

CONSTANTINOU AC and FENTON N (2018). Things to know about Bayesian networks. Royal Statistical Society  Significance  10.1111/j.1740-9713.2018.01126.x

YET B, NEIL M, FENTON N, CONSTANTINOU AC and DEMENTIEV E (2018). An Improved Method for Solving Hybrid Influence Diagrams. Elsevier  International Journal of Approximate Reasoning  10.1016/j.ijar.2018.01.006

Yet B, Constantinou A, Fenton N and Neil M (2018). Expected Value of Partial Perfect Information in Hybrid Models Using Dynamic Discretization. Ieee Access  vol. 6, 7802-7817. 10.1109/ACCESS.2018.2799527

2017

CONSTANTINOU AC and Fenton N (2017). The future of the London Buy-To-Let property market: Simulation with temporal Bayesian Networks. Public Library of Science (Plos)  Plos One  vol. 12, (6) 10.1371/journal.pone.0179297

CONSTANTINOU AC and Fenton NORMAN (2017). Towards Smart-Data: Improving predictive accuracy in long-term football team performance. Elsevier  Knowledge-Based Systems  10.1016/j.knosys.2017.03.005

2016

Fenton N, Neil M, Lagnado D, William M, Yet B and CONSTANTINOU AC (2016). How to model mutually exclusive events based on independent causal pathways in Bayesian network models. Knowledge-Based Systems  10.1016/j.knosys.2016.09.012

Yet B, CONSTANTINOU AC, Fenton N, Neil M, Luedeling E and Shepherd K (2016). A Bayesian Network Framework for Project Cost, Benefit and Risk Analysis with an Agricultural Development Case Study. Expert Systems With Applications  10.1016/j.eswa.2016.05.005

CONSTANTINOU AC, FENTON N and NEIL M (2016). Integrating expert knowledge with data in Bayesian networks: Preserving data-driven expectations when the expert variables remain unobserved. Expert Systems With Applications  10.1016/j.eswa.2016.02.050

Constantinou AC, Fenton N, Marsh W and Radlinski L (2016). From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support. Artificial Intelligence in Medicine  vol. 67, 75-93. 10.1016/j.artmed.2016.01.002

2015

CONSTANTINOU AC, Freestone M, Marsh W and Coid J (2015). Causal inference for violence risk management and decision support in forensic psychiatry. Decision Support Systems  vol. 80, 42-55. 10.1016/j.dss.2015.09.006

Constantinou AC, Yet B, Fenton N, Neil M and Marsh W (2015). Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences. Elsevier  Artificial Intelligence in Medicine  vol. 66, 41-52. 10.1016/j.artmed.2015.09.002

Constantinou AC, Freestone M, Marsh W, Fenton NE and Coid J (2015). Risk assessment and risk management of violent re-offending among prisoners. Expert Systems With Applications  vol. 42, (21) 10.1016/j.eswa.2015.05.025

2014

Constantinou AC, Fenton NE and Hunter Pollock LJ (2014). Bayesian networks for unbiased assessment of referee bias in Association Football. Psychology of Sport and Exercise  vol. 15, (5) 538-547. 10.1016/j.psychsport.2014.05.009

2013

Constantinou AC, Fenton NE and Neil M (2013). Profiting from an inefficient association football gambling market: Prediction, risk and uncertainty using Bayesian networks. Knowledge-Based Systems  vol. 50, 60-86. 10.1016/j.knosys.2013.05.008

Constantinou AC and Fenton NE (2013). Profiting from arbitrage and odds biases of the European football gambling market. The Journal of Gambling Business and Economics  vol. 7, (2) 41-70. 10.5750/jgbe.v7i2.630

Constantinou A and FENTON NE (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  vol. 9, (1) 37-50. 10.1515/jqas-2012-0036

2012

Constantinou A, FENTON NE and Neil M (2012). pi-football: A Bayesian network model for forecasting Association Football match outcomes. Elsevier  Knowledge Based Systems  vol. 36, 322-339. 10.1016/j.knosys.2012.07.008

Constantinou A and FENTON NE (2012). Solving the problem of inadequate scoring rules for assessing probabilistic football forecasting models. Journal of Quantitative Analysis in Sports  vol. 8, (1) 10.1515/1559-0410.1418

Grants

Grants of specific relevance to Applied Data Science

Bayesian Artificial Intelligence for Decision Making under Uncertainty (BAYES-AI)
Constantinou AC
£475,818 Engineering and Physical Sciences Research Council (31-05-2018 - 30-05-2021)

Summary