Dr Anthony Constantinou

Teaching

Data Analytics (Undergraduate)

This module focuses on the range of approaches, methodologies, techniques and tools for data analysis, and the use of data analysis findings to inform decision-making in an industrial / business context.

Data Analytics (Postgraduate/Undergraduate)

This module focuses on the range of approaches, methodologies, techniques and tools for data analysis, and the use of data analysis findings to inform decision-making in an industrial / business context. It exposes students to a range of industry-standard statistical and data analysis techniques and tools, and fosters awareness of the challenges associated with working with large datasets. The module also covers topics related to the legal, social, ethical and professional issues associated with data storage and analysis. Students will undertake practical work including empirical data analysis and summarisation / presentation of the results to a range of relevant stakeholders.

Research

Research Interests:

Publications

• Fenton N, Neil M, Constantinou A (2020). The Book of Why: The New Science of Cause and Effect, Judea Pearl, Dana Mackenzie, Basic Books (2018). nameOfConference

  
  

  DOI: 10.1016/j.artint.2020.103286

  
  

  QMRO: qmroHref
• Constantinou A (2020). Learning Bayesian Networks with the Saiyan algorithm. nameOfConference

  
  

  DOI: 10.1145/3385655

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/64081
• Guo Z, Constantinou AC (2020). Approximate learning of high dimensional Bayesian network structures via pruning of Candidate Parent Sets.. nameOfConference

  
  

  DOI: doi

  
  

  QMRO: qmroHref
• Constantinou AC (2020). Asian Handicap football betting with Rating-based Hybrid Bayesian Networks.. nameOfConference

  
  

  DOI: doi

  
  

  QMRO: qmroHref
• Chobtham K, Constantinou AC (2020). Bayesian network structure learning with causal effects in the presence of latent variables.. nameOfConference

  
  

  DOI: doi

  
  

  QMRO: qmroHref
• Constantinou AC, Liu Y, Chobtham K et al. (2020). Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data.. nameOfConference

  
  

  DOI: doi

  
  

  QMRO: qmroHref
• Constantinou AC (2020). Learning Bayesian Networks that enable full propagation of evidence.. nameOfConference

  
  

  DOI: doi

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/65322
• Constantinou AC (2019). Evaluating structure learning algorithms with a balanced scoring function.. nameOfConference

  
  

  DOI: doi

  
  

  QMRO: qmroHref
• Kitson NK, Constantinou AC (2019). Learning Bayesian networks from demographic and health survey data.. nameOfConference

  
  

  DOI: doi

  
  

  QMRO: qmroHref
• Fenton NE, Neil M, Constantinou AC (2019). Simpson's Paradox and the implications for medical trials.. nameOfConference

  
  

  DOI: doi

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/62666
• CONSTANTINOU AC (2018). Dolores: A model that predicts football match outcomes from all over the world. nameOfConference

  
  

  DOI: 10.1007/s10994-018-5703-7

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/38063
• CONSTANTINOU AC, FENTON N (2018). Things to know about Bayesian networks. nameOfConference

  
  

  DOI: 10.1111/j.1740-9713.2018.01126.x

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/36560
• YET B, NEIL M, FENTON N et al. (2018). An Improved Method for Solving Hybrid Influence Diagrams. nameOfConference

  
  

  DOI: 10.1016/j.ijar.2018.01.006

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/32419
• Yet B, Constantinou A, Fenton N et al. (2018). Expected Value of Partial Perfect Information in Hybrid Models Using Dynamic Discretization. nameOfConference

  
  

  DOI: 10.1109/ACCESS.2018.2799527

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/32418
• CONSTANTINOU AC, Fenton N (2017). The future of the London Buy-To-Let property market: Simulation with temporal Bayesian Networks. nameOfConference

  
  

  DOI: 10.1371/journal.pone.0179297

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/24776
• CONSTANTINOU AC, Fenton NORMAN (2017). Towards Smart-Data: Improving predictive accuracy in long-term football team performance. nameOfConference

  
  

  DOI: 10.1016/j.knosys.2017.03.005

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/22040
• Fenton N, Neil M, Lagnado D et al. (2016). How to model mutually exclusive events based on independent causal pathways in Bayesian network models. nameOfConference

  
  

  DOI: 10.1016/j.knosys.2016.09.012

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/15923
• Yet B, CONSTANTINOU AC, Fenton N et al. (2016). A Bayesian Network Framework for Project Cost, Benefit and Risk Analysis with an Agricultural Development Case Study. nameOfConference

  
  

  DOI: 10.1016/j.eswa.2016.05.005

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/12375
• CONSTANTINOU AC, FENTON N, NEIL M (2016). Integrating expert knowledge with data in Bayesian networks: Preserving data-driven expectations when the expert variables remain unobserved. nameOfConference

  
  

  DOI: 10.1016/j.eswa.2016.02.050

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/11586
• Constantinou AC, Fenton N, Marsh W et al. (2016). From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support. nameOfConference

  
  

  DOI: 10.1016/j.artmed.2016.01.002

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/11587
• Constantinou AC, Fenton NE (2016). Improving Predictive Accuracy Using Smart-Data rather than Big-Data: A Case Study of Soccer Teams' Evolving Performance.. nameOfConference

  
  

  DOI: doi

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/14960
• CONSTANTINOU AC, Freestone M, Marsh W et al. (2015). Causal inference for violence risk management and decision support in forensic psychiatry. nameOfConference

  
  

  DOI: 10.1016/j.dss.2015.09.006

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/10774
• Constantinou AC, Yet B, Fenton N et al. (2015). Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences. nameOfConference

  
  

  DOI: 10.1016/j.artmed.2015.09.002

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/10760
• Constantinou AC, Freestone M, Marsh W et al. (2015). Risk assessment and risk management of violent re-offending among prisoners. nameOfConference

  
  

  DOI: 10.1016/j.eswa.2015.05.025

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/15804
• Constantinou AC, Fenton NE, Hunter Pollock LJ (2014). Bayesian networks for unbiased assessment of referee bias in Association Football. nameOfConference

  
  

  DOI: 10.1016/j.psychsport.2014.05.009

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/10772
• Constantinou AC, Fenton NE, Neil M (2013). Profiting from an inefficient association football gambling market: Prediction, risk and uncertainty using Bayesian networks. nameOfConference

  
  

  DOI: 10.1016/j.knosys.2013.05.008

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/10778
• Constantinou A, FENTON NE (2013). Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries. nameOfConference

  
  

  DOI: 10.1515/jqas-2012-0036

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/10776
• Constantinou A, Fenton N (2013). PROFITING FROM ARBITRAGE AND ODDS BIASES OF THE EUROPEAN FOOTBALL GAMBLING MARKET. nameOfConference

  
  

  DOI: doi

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/10781
• Constantinou A, FENTON NE, Neil M (2012). pi-football: A Bayesian network model for forecasting Association Football match outcomes. nameOfConference

  
  

  DOI: 10.1016/j.knosys.2012.07.008

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/10780
• Constantinou A, FENTON NE (2012). Solving the problem of inadequate scoring rules for assessing probabilistic football forecasting models. nameOfConference

  
  

  DOI: 10.1515/1559-0410.1418

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/10783