photo

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

Contact Information
Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA)
Polo Universitario Lugano
Via la Santa 1
6962 Lugano (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

Old books and book editions are available here.

Book Chapters

Old book chapters are available here.

Refereed Journal Articles

In the works

  • A Tutorial on Bayesian Networks for Psychopathology Researchers. [  ]
    G. Briganti and M. Scutari and R. J. McNally.
    Psychological Methods.
  • Achieving Fairness with a Simple Ridge Penalty.arXiv ]
    M. Scutari, F. Panero and M. Proissl.
    Statistics and Computing.
  • Do Short-Term Effects Predict Long-Term Improvements in Women who Receive Manual Therapy or Surgery for Carpal Tunnel Syndrome? A Bayesian Network Analysis of a Randomized Clinical Trial. [  ]
    B. X. W. Liew, A. I. de-la-Llave-Rincón, M. Scutari, J. L. Arias-Buría, C. E. Cook, J. Cleland and C. Fernández-de-las-Peñas.
    Physical Therapy.
  • Path Analysis Models Integrating Psychological, Neuro-Physiological and Clinical Variables in Individuals with Tension-Type Headache. [  ]
    B. X. W. Liew, M. Palacios-Ceña, M. Scutari, S. Fuensalida-Novo, A. Guerrero-Peral, C. Ordás-Bandera, J. A. Pareja and C. Fernández-de-las-Peñas.
    Cephalalgia.

In print

  • A Bayesian Hierarchical Score for Structure Learning from Related Data Sets.arXiv ]
    L. Azzimonti, G. Corani and M. Scutari (2021).
    International Journal of Approximate Reasoning. This is an extended version of the “Structure Learning with a Hierarchical Bayesian Score” PMLR paper.

Published

  • How Does Individualised Physiotherapy Work for People with Low Back Pain? A Bayesian Network Analysis Using Randomised Controlled Trial Data.html | pdf | doi ]
    B. X. W. Liew, J. J. Ford and M. Scutari and A. J. Hahne (2021).
    PLoS ONE, 16(10), 1–16.
  • Learning Bayesian Networks from Incomplete Data with the Node-Averaged Likelihood.arXiv | html | pdf | doi ]
    T. Bodewes and M. Scutari (2021).
    International Journal of Approximate Reasoning, 138, 145–160. This is an extended version of the “Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete Data” PMLR paper.
  • A Constraint-Based Algorithm for the Structural Learning of Continuous-Time Bayesian Networks.arXiv | html | pdf | doi ]
    A. Bregoli, M. Scutari and F. Stella (2021).
    International Journal of Approximate Reasoning, 138, 105–122 This is an extended version of the “Constraint-Based Learning for Continuous-Time Bayesian Networks” PMLR paper.
  • Self-Efficacy Beliefs and Pain Catastrophizing Mediate Between Pain Intensity and Pain Interference in Whiplash-Associated Disorders.html | pdf | doi ]
    Y. Pedrero-Martin, J. Martinez-Calderon, D. Falla, B. X. W. Liew, M. Scutari and A. Luque-Suarez (2021).
    The Clinical Journal of Pain, 30, 1689–1698.
  • Network Structures of Symptoms from the Zung Depression Scale.PsyArXiv (preprint) | html | online supplementary material | doi ]
    G. Briganti, M. Scutari and P. Linkowski (2021).
    Psychological Reports, 124(4), 1897–1911.
  • Mechanisms of Recovery after Neck-Specific or General Exercises in Patients with Cervical Radiculopathy.html | pdf | doi ]
    B. X. W. Liew, A. Peolsson, D. Falla, J. A. Cleland, M. Scutari, M. Kierkegaard, Å. Dedering (2021).
    European Journal of Pain, 25(5), 1162–1172.
  • Constraint-Based Learning for Continuous-Time Bayesian Networks.arXiv (preprint) | html | pdf ]
    A. Bregoli, M. Scutari and F. Stella (2020).
    Proceedings of Machine Learning Research, 138 (PGM 2020), 41–52.
  • Structure Learning with a Hierarchical Bayesian Score.arXiv (preprint) | html | pdf ]
    L. Azzimonti, G. Corani and M. Scutari (2020).
    Proceedings of Machine Learning Research, 138 (PGM 2020), 5–16.
  • Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete Data.arXiv (preprint) | html | pdf ]
    T. Bodewes and M. Scutari (2020).
    Proceedings of Machine Learning Research, 138 (PGM 2020), 29–40.
  • Hard and Soft EM in Bayesian Network Learning from Incomplete Data.arXiv (preprint) | html | pdf | doi ]
    A. Ruggieri and F. Stranieri and F. Stella and M Scutari (2020).
    Algorithms, 13(12):329, 1—16.
  • An Interdisciplinary Examination of Stress and Injury Occurrence in Athletes.html | pdf | doi ]
    H. Fisher, M. Gittoes, L. Evans, L. Bitchell, R. Mullen and M. Scutari (2020).
    Frontiers in Sports and Active Living, 2(595619), 1–20.
  • A Machine Learning Approach to Relationships Among Alexithymia Components.pdf ]
    G. Briganti, M. Scutari and P. Linkowski (2020).
    Psychiatria Danubina, 32(Suppl. 1), 180–187.
  • 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 | pdf | 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

  • Bayesian Networks: How We Can Use Them as Probabilistic and Causal Models.pdf ]
    Digital Health Lab Day, Zurich University of Applied Sciences (ZHAW), Winterthur (September 16, 2021).
  • Mapping Complex Data with Bayesian Networks.pdf ]
    Spring Meeting of the Dutch Statistical Society (May 21, 2021).
  • Bayesian Networks and their Extensions in Modern Machine Learning.pdf (long version) | pdf (short version) ]
    Department of Economics and Management, Univesity of Brescia (April 8, 2021). And again at the Department of Economics, University of Crete (October 13, 2021).
  • What is Machine Learning?pdf ]
    Centre for Doctoral Training, European School of Molecular Medicine, European Institute of Oncology (March 8, 2021).
  • Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete Data.pdf ]
    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). And again at the Centre for Doctoral Training, European School of Molecular Medicine, European Institute of Oncology (March 9, 2021).
  • 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).

Older talks from 2015 to 2018 are available here.

Older talks from up to 2014 are available here.