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  • A Decision Support System for Sustainable Agriculture and Food Loss Reduction under Uncertain~Agricultural Policy Frameworks Martine J. Barons, Lael E. Walsh, Edward E. Salakpi and Linda Nichols Agriculture



  • Communicating Climate Risk: A Toolkit Walton, J.L., Levontin, P., Barons, M.J., Workman, M., Mackie, E., and Kleineberg, J. (London, UK: AU4DM, 2022). This Toolkit is licensed under a CC BY-NC-SA 4.0 licence. 2nd edition, October 2022 isbn: 978-1-912802-07-4 doi: Also:
  • Safeguarding the Nation’s Digital Memory: Bayesian Network modelling of digital preservation risks Martine J. Barons, Thais C. O. Fonseca, Hannah Merwood and David H. Underdown [submitted versionLink opens in a new window ](Accepted Jan 2022, Proceedings of ECMI 2021 Consortium for Mathematics in Industry Pages 501-508) hereLink opens in a new window 2022 Winner Link opens in a new windowof Decision Analysis Practice Award




  •  Report of the FARM food poverty workshop held at the University of Warwick on 02 May 2019, Barons, M. J., Garthwaite, K., Jolly, A. & Price, C. 2019 Food Action & Research Midlands. Available here.Link opens in a new window



  • Eliciting Probabilistic Judgements for Integrating Decision Support Systems. Barons, M. J., Wright, S. K. & Smith, J. Q. in Elicitation: The science and art of structuring judgement, Dias LC, Morton A, Quigley J (eds), Springer, New York, 2018 (Chapter 17)SpringerLink opens in a new window


  • Coherent Frameworks for Statistical Inference serving Integrating Decision Support Systems Jim Q. Smith, Martine J. Barons, Manuele Leonelli. 2016 arXivLink opens in a new window
  • A marked renewal process for the size of a honey bee colony Aihua Xia, Richard M. Huggins, Martine J. Barons, Lois Guilloit. 2016 arXivLink opens in a new window



  • Dynamic Bayesian Networks for decision support and sugar food security Barons, M.J., Zhong, X., and Smith, J.Q. (2014) Technical report, University of Warwick CRiSM research report Paper No. 14-18. CRiSMLink opens in a new window Submitted to Applied Artificial Intelligence August 2014.
  • Matching patients to an intervention for back pain: classifying patients using a latent class approach Martine J. Barons PhD, Frances E. Griffiths MRCGP PhD, Nick Parsons PhD, Anca Alba PhD, Margaret Thorogood PhD, Graham F. Medley PhD and Sarah E. Lamb PhD DOI: 10.1111/jep.12115 Journal of Evaluation in Clinical Practice, 20 (2014) 544–550 ISSN 1365-2753. pdfLink opens in a new window

    Article first published online: 24 MAR 2014


  • A Comparison of Artificial Neural Network, Latent Class Analysis and Logistic Regression for Determining Which Patients Benefit from a Cognitive Behavioural Approach to Treatment for Non-Specific Low Back Pain. Martine J. Barons, Nick Parsons, Frances Griffiths, Margaret Thorogood. 04/2013; DOI:10.1109/CICARE.2013.6583061 In proceeding of: IEEE Symposium Series on Computational Intelligence, At Singapore, Volume: 2013 CICARE2013Link opens in a new window
  • PhD Thesis: "What is the added value of using non-linear models to explore complex healthcare datasets?" Martine J. Barons. Examined 25th June 2013. Warwick Research Archive Portal (WRAP) PhD ThesisLink opens in a new window