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From 2019 to 2022 (IDSIA)

2022

• Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach. [ pdf ]
  A. Zanga, A. Bernasconi, P. Lucas, H. Pijnenborg, C. Reijnen, M. Scutari and F. Stella (2022).
  Proceedings of the 1st Workshop on Artificial Intelligence for Healthcare, 21st International Conference of the Italian Association for Artificial Intelligence (AIxIA 2022), 1–15.
• Using Mixed-Effect Models to Learn Bayesian Networks from Related Data Sets. [ arXiv (preprint) | html | pdf ]
  M. Scutari, C. Marquis and L. Azzimonti (2022).
  Proceedings of Machine Learning Research 186 (PGM 2022), 73–84.
• Achieving Fairness with a Simple Ridge Penalty. [ arXiv (preprint) | html | pdf | doi ]
  M. Scutari, F. Panero and M. Proissl (2022).
  Statistics and Computing, 32, 77.
• Comments on: "Hybrid Semiparametric Bayesian Networks". [ arXiv (preprint) | html | pdf | doi ]
  M. Scutari (2022).
  TEST, 30, 328–330.
• Bayesian Network Analysis Reveals the Interplay of Intracranial Aneurysm Rupture Risk Factors. [ html | pdf | doi ]
  M. Delucchi, G. R. Spinner, M. Scutari, P. Bijlenga, S.Morel, C. M. Friedrich, R. Furrer and S. Hirsch (2022).
  Computers in Biology and Medicine, 147, 105740.
• 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. [ html | doi ]
  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 (2022).
  Physical Therapy, 102(4), pzac015.

2021

• A Bayesian Hierarchical Score for Structure Learning from Related Data Sets. [ arXiv (preprint) | html | pdf | doi ]
  L. Azzimonti, G. Corani and M. Scutari (2021).
  International Journal of Approximate Reasoning, 142, 248–265. This is an extended version of the “Structure Learning with a Hierarchical Bayesian Score” PMLR paper.
• 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, 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 (preprint) | 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 (preprint) | 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 | pdf | 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.

2020

• 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, F. Stranieri, 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 Science, 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.

2019

• 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.