photo

Marco Scutari, Ph.D.
Senior Researcher in Bayesian Networks and Graphical Models

Contact Information
Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA)
Galleria 2, Via Cantonale 2c
6928 Manno (Switzerland)
work email: scutari@idsia.ch
personal email: marco.scutari@gmail.com

Here are my curriculum vitae, my Google Scholar profile and my arXiv preprints.

Publications

Books

  • Réseaux Bayésiens avec R: Élaboration, Manipulation et Utilisation en Modélisation Appliquée.Amazon ]
    J.-B. Denis and M. Scutari (2014).
    Pratique R, EDP. This is a French translation of the English book above.

Book Chapters

Old book chapters are available here.

Refereed Journal Articles

In the works

  • An Interdisciplinary Examination of Stress and Injury Occurrence in Athletes. [  ]
    H. Fisher, M. Gittoes, L. Evans, R. Mullen, L. Bitchell and M. Scutari (submitted).
    PLoS ONE.
  • Self-Efficacy Beliefs and Pain Catastrophizing Mediate Between Pain Intensity and Pain Interference in Whiplash-Associated Disorders [  ]
    Y. Pedrero-Martin, J. Martinez-Calderon, D. Falla, B. X. W. Liew, M. Scutari and A. Luque-Suarez (submitted).
    The Clinical Journal of Pain.

Published

  • A Machine Learning Approach to Relationships Among Alexithymia Components. [  ]
    G. Briganti, M. Scutari and P. Linkowski (in print).
    Psychiatria Danubina.
  • Constraint-Based Learning for Continuous-Time Bayesian Networks.arXiv (preprint) ]
    A. Bregoli, M. Scutari and F. Stella (in print).
    Proceedings of Machine Learning Research (PGM 2020).
  • Structure Learning with a Hierarchical Bayesian Score.arXiv (preprint) ]
    L. Azzimonti, G. Corani and M. Scutari (in print).
    Proceedings of Machine Learning Research (PGM 2020).
  • Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete Data.arXiv (preprint) ]
    T. Bodewes and M. Scutari (in print).
    Proceedings of Machine Learning Research (PGM 2020).
  • A Bayesian Network Approach to Symptoms from the Zung Depression Scale.PsyArXiv (preprint) | doi ]
    G. Briganti, M. Scutari and P. Linkowski (in print).
    Psychological Reports.
  • Tectonic Control on Global Variations in the Record of Large-Magnitude Explosive Eruptions in Volcanic Arcs.html | pdf | doi ]
    T. E. Sheldrake, L. Caricchi and M. Scutari (2020).
    Frontiers in Earth Sciences, 8:127, 1–14.
  • Bayesian Network Models for Incomplete and Dynamic Data.arXiv (preprint) | html | pdf | doi ]
    M. Scutari (2020).
    Statistica Neerlandica, 74(3):397–419.
  • Probing the Mechanisms Underpinning Recovery in Post-Surgical Patients with Cervical Radiculopathy Using Bayesian Networks.html | doi ]
    B. X. W. Liew, A. Peolsson, M. Scutari, H. Löfgren, J. Wibault, Å. Dedering, B. Öberg, P. Zsigmond and D. Falla (2020).
    European Journal of Pain, 24(5):909–920.
  • Who Learns Better Bayesian Network Structures: Accuracy and Speed of Structure Learning Algorithms.arXiv (preprint) | html | pdf | online supplementary material | doi ]
    M. Scutari, C. E. Graafland and J. M. Gutiérrez (2019).
    International Journal of Approximate Reasoning, 115:235–253. This is an extended version of the “Who Learns Better Bayesian Network Structures: Constraint-Based, Score-Based or Hybrid Algorithms?” PMLR paper.
  • Investigating the Causal Mechanisms of Symptom Recovery in Chronic Whiplash Associated Disorders using Bayesian Networks.html | doi ]
    B. X. W. Liew, M. Scutari, A. Peolsson, G. Peterson, M. L. Ludvigsson and D. Falla (2019).
    The Clinical Journal of Pain, 35(8):647–655.
  • Learning Bayesian Networks from Big Data with Greedy Search: Computational Complexity and Efficient Implementation.arXiv (preprint) | html | pdf | online supplementary material | doi ]
    M. Scutari, C. Vitolo and A. Tucker (2019).
    Statistics and Computing, 29(5):1095-1108.
  • 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.
  • 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.
  • 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.

Older papers from up to 2014 are available here.

Ph.D. Dissertation and Technical Reports

The material from my Ph.D. dissertation is available here.

Teaching Material

My teaching material is available here.

Invited Talks, (Short) Course Slides, Conference Presentations and Posters

  • Structure Learning with a Hierarchical Bayesian Score. [ ]
    10th International Conference on Probabilistic Graphical Models (PGM), Aalborg (September 23-25, 2020).
  • Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete Data. [ ]
    10th International Conference on Probabilistic Graphical Models (PGM), Aalborg (September 23-25, 2020).
  • The Regional Dimension: a Bayesian Network Analysis.pdf ]
    Technical Workshop on “Analytical Tools for Capacity Building on Quantitative Methods for SDG Interaction and Integration in National Development Strategies and Integrated Planning”, United Nations Economic Commission for Africa (UNECA), Addis Ababa (December 18, 2019).
  • Challenges in Bayesian Network Modelling of Climate and Weather Data.pdf ]
    The 1st Artificial Intelligence for Copernicus Workshop. European Centre for Medium-Range Weather Forecasts, Reading (November 6, 2019).
  • bnlearn: Practical Bayesian Networks in R.html ]
    useR! Conference, Toulouse (July 9, 2019).
  • Bayesian Networks, Big Data and Greedy Search: Efficient Implementation with Classic Statistics.pdf ]
    Department of Systems Innovation, Graduate School of Engineering Science, Osaka University (April 3, 2019).
  • Who Learns Better Bayesian Network Structures: Constraint-Based, Score-Based or Hybrid Algorithms?pdf ]
    9th International Conference on Probabilistic Graphical Models (PGM), Prague (September 11-14, 2018).
  • Dirichlet Bayesian Network Scores and the Maximum Entropy Principle.pdf ]
    3rd Workshop on “Advanced Methodologies for Bayesian Networks” (AMBN), Kyoto University (September 20-22, 2017). And again at the Department of Mathematics, Brunel University (November 24, 2017). And again at the 10th Conference of the ERCIM WG on Computational and Methodological Statistics, London (December 16-18, 2017).
  • bnlearn, Learning Bayesian Networks 10 Years Later.pdf ]
    Workshop on “Bayesian Networks Tools”, Satellite event of AMBN, Tokyo (September 19, 2017).
  • Beyond Uniform Priors in Bayesian Network Structure Learning.pdf ]
    Workshop on “Learning Graphical Models in High Dimensions”, ICMS, Edinburgh (April 4-7, 2017).
  • Bayesian Network Modelling: with Examples.pdf ]
    IBM Analytics, London Data Science Studio (November 28, 2016).
  • Bayesian Network Modelling: with Examples in Genetics and Systems Biology.pdf ]
    Bayesian Networks Meetup, Alan Turing Institute, London (September 29, 2016).
  • An Empirical-Bayes Score for Discrete Bayesian Networks.pdf ]
    8th International Conference on Probabilistic Graphical Models (PGM), Lugano (September 6-9, 2016). And again at the Department of Informatics, Systems and Communication, University of Milano Bicocca (January 17, 2017).
  • Bayesian Networks, MAGIC Populations and Mutliple Trait Prediction.pdf ]
    5th International Conference on Quantitative Genetics (ICQG), Madison (June 12-17, 2016). And again as a poster at the 2nd Probabilistic Modeling in Genomics Conference, Oxford, (ProbGen, September 12-14, 2016). And again at the School of Agriculture, Food, and Rural Development, Newcastle University (November 16, 2016). And again at the workshop “Learning Graphical Models in High Dimensions”, ICMS, Edinburgh (April 4-7, 2017).
  • Using Genetic Distance to Infer the Accuracy of Genomic Prediction.pdf ]
    Statistical Omics (STOMICS) Meeting Series, Imperial College (September 7, 2015).
  • Modelling Survey Data with Bayesian Networks.pdf ]
    Workshop “Bayesian Networks at Work”, Data Methods and Systems Statistical Laboratory, University of Brescia (May 18, 2015).

Older talks from up to 2014 are available here.