From 2015 to 2018 (Oxford)

2018

  • Who Learns Better Bayesian Network Structures: Constraint-Based, Score-Based or Hybrid Algorithms? [ arXiv (preprint) | html | pdf | online supplementary material ]
    M. Scutari, C. E. Graafland and J. M. Gutiérrez (2018).
    Proceedings of Machine Learning Research, 72 (PGM 2018), 416–427.
  • A Network Perspective of Engaging Patients in Specialist and Chronic Illness Care: the 2014 International Health Policy Survey. [ html | pdf | doi ]
    Y.-S. Chao, M. Scutari, T.-S. Chen, C.-J. Wu, M. Durand, A. Boivin, H.-S. Wu and W.-C. Chen (2018).
    PLoS ONE, 13(8):e0201355, 1–12.
  • Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle. [ arXiv (preprint) | html | pdf | doi ]
    M. Scutari (2018).
    Behaviormetrika, 45(2), 337–362. This is an extended version of the “Dirichlet Bayesian Network Scores and the Maximum Entropy Principle” PMLR paper.
  • Modelling Air Pollution, Climate and Health Data Using Bayesian Networks: a Case Study of the English Regions. [ html | pdf | online supplementary material | doi ]
    C. Vitolo, M. Scutari, M. Ghalaieny, A. Tucker and A. Russell (2018).
    Earth and Space Science, 5(4), 76–88.

2017

  • Bayesian Networks Analysis of Malocclusion Data. [ arXiv (preprint) | html | pdf | online supplementary material | doi ]
    M. Scutari, P. Auconi, G. Caldarelli and L. Franchi (2017).
    Scientific Reports, 7(15326).
  • Dirichlet Bayesian Network Scores and the Maximum Entropy Principle. [ arXiv (preprint) | html | pdf ]
    M. Scutari (2017).
    Proceedings of Machine Learning Research, 73 (AMBN 2017), 8–20.
  • A Network Perspective on Patient Experiences and Health Status: the Medical Expenditure Panel Survey 2004 to 2011. [ html | pdf | doi ]
    Y.-S. Chao, H.-T. Wu, M. Scutari, T.-S. Chen, C.-J. Wu, M. Durand and A. Boivin (2017).
    BMC Health Services Research, 17(579), 1–12.
  • Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Package. [ arXiv (preprint) | html | pdf | online supplementary material | doi ]
    M. Scutari (2017).
    Journal of Statistical Software, 77(2), 1–20.

2016

  • Using Genetic Distance to Infer the Accuracy of Genomic Prediction. [ arXiv (preprint) | html | pdf | doi ]
    M. Scutari, I. Mackay and D. J. Balding (2016).
    PLoS Genetics, 12(9):e1006288, 1–19.
  • An Empirical-Bayes Score for Discrete Bayesian Networks. [ arXiv (preprint) | html | pdf | online supplementary material ]
    M. Scutari (2016).
    Journal of Machine Learning Research, 52 (Proceedings Track, PGM 2016), 438–448.

2015

None.