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
CH-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
- Bayesian Networks in R with Applications in Systems Biology.
[ Springer |
Amazon |
data, code and errata ]
R. Nagarajan, M. Scutari and S. Lèbre (2013).
Use R!, Springer (US).
- Bayesian Networks with Examples in R.
[ CRC |
Amazon |
data, code and errata ]
M. Scutari and J.-B. Denis (2014).
Chapman & Hall.
- 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
- Introduction to Graphical Modelling.
[ arXiv (preprint) |
Wiley |
Amazon ]
M. Scutari and K. Strimmer (2011).
in Handbook of Statistical Systems Biology, D. J. Balding, M. Stumpf and M. Girolami (editors).
Wiley.
- Personalised Medicine: Taking a New Look at the Patient. [ arXiv (preprint) | Springer | Amazon ]
- Graphical Modelling in Genetics and Systems Biology.
[ arXiv (preprint) |
Springer |
Amazon ]
M. Scutari (2015).
in Foundations of Biomedical Knowledge Representation: Methods and Applications, A. Sommerson and P. Lucas (editors).
Lecture Notes in Artificial Intelligence, Springer.
Refereed Journal Articles
In the works
- Who Learns Better Bayesian Network Structures: Accuracy and Speed of Structure Learning Algorithms.
[ ]
M. Scutari, C. E. Graafland and J. M. Gutierrez (submitted).
International Journal of Approximate Reasoning. This is an extended version of the “Who Learns Better Bayesian Network Structures: Constraint-Based, Score-Based or Hybrid Algorithms?” PMLR paper. - A Bayesian Network Approach to Symptoms from the Zung Depression Scale.
[ ]
G. Briganti, M. Scutari and P. Linkowski (submitted).
Journal of Affective Disorders. - Investigating the Causal Mechanisms of Symptom Recovery in Chronic Whiplash Associated Disorders using Bayesian Networks.
[ ]
A. Peolsson, M. L. Ludvigsson, G. Peterson, M. Scutari and D. Falla (submitted).
The Clinical Journal of Pain.
Published
- Learning Bayesian Networks from Big Data with Greedy Search: Computational Complexity and Efficient Implementation.
[ arXiv (preprint) |
html |
pdf ]
M. Scutari, C. Vitolo and A. Tucker (2019).
Statistics and Computing, online first. - Who Learns Better Bayesian Network Structures: Constraint-Based, Score-Based or Hybrid Algorithms?
[ arXiv (preprint) |
html |
pdf ]
M. Scutari, C. E. Graafland and J. M. Gutierrez (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 ]
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. - Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle.
[ arXiv (preprint) |
html |
pdf ]
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 ]
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 ]
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 ]
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 ]
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 ]
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. - Applying Association Mapping and Genomic Selection to the Dissection of Key Traits in Elite European Wheat.
[ html |
pdf ]
A. R. Bentley, M. Scutari, N. Gosman, S. Faure, F. Bedford, P. Howell, J. Cockram, G. A. Rose, T. Barber, R. Horsnell, C. Pumfrey, E. Winnie, J. Shacht, K. Beauchêne, S. Praud, A. Greenland, D. J. Balding and I. Mackay (2014).
Theoretical and Applied Genetics, 127(12), 2619–2633. - Multiple Quantitative Trait Analysis Using Bayesian Networks.
[ arXiv (preprint) |
html |
pdf |
online supplementary material ]
M. Scutari, P. Howell, D. J. Balding and I. Mackay (2014).
Genetics, 198(1), 129–137. - Crossed Linear Gaussian Bayesian Networks, Parsimonious Models.
[ html |
pdf ]
S. Tian, M. Scutari and J.-B. Denis (2014).
Journal de la Société Française de Statistique, 155(3), 1–21. - Impact of Noise on Inferring Molecular Associations and Networks.
[ html |
pdf ]
R. Nagarajan and M. Scutari (2013).
PLoS ONE, 8(12):e80735, 1–12. - On the Prior and Posterior Distributions Used in Graphical Modelling (with discussion).
[ arXiv (preprint) |
html |
pdf ]
M. Scutari (2013).
Bayesian Analysis, 8(3), 505–532. The discussion is here, here and here, and the rejonder is here. - Improving the Efficiency of Genomic Selection.
[ arXiv (preprint) |
html |
pdf ]
M .Scutari, I. Mackay and D. J. Balding (2013).
Statistical Applications in Genetics and Molecular Biology, 12(4), 517–527. - On Identifying Significant Edges in Graphical Models of Molecular Networks.
[ arXiv (preprint) |
html |
pdf ]
M. Scutari and R. Nagarajan (2013).
Artificial Intelligence in Medicine, 57(3), 207-217. Special Issue containing selected papers from the Workshop “Probabilistic Problem Solving in Biomedicine, 13th Conference on Artificial Intelligence in Medicine (AIME'11)”, Bled (Slovenia), July 2, 2011. - Bayesian Network Structure Learning with Permutation Tests.
[ arXiv (preprint) |
html |
pdf ]
M. Scutari and A. Brogini (2012).
Communications in Statistics–Theory & Methods, 41(16-17), 3233-3243. Special Issue “Statistics for Complex Problems: Permutation Testing Methods and Related Topics”. Proceedings of the Conference “Statistics for Complex Problems: the Multivariate Permutation Approach and Related Topics”, Padova, June 14–15, 2010. - Functional Relationships Between Genes Associated with Differentiation Potential of Aged Myogenic Progenitors.
[ html |
pdf ]
R. Nagarajan, S. Datta, M. Scutari, M. L. Beggs, G. T. Nolen and C. A. Peterson (2010).
Frontiers in Physiology (Systems Biology section), 1(21), 1–8. - Learning Bayesian Networks with the bnlearn R Package.
[ arXiv (preprint) |
html |
pdf ]
M. Scutari (2010).
Journal of Statistical Software, 35(3), 1–22. - NATbox: a Network Analysis Toolbox in R.
[ html |
pdf ]
S. S. Chavan, M. A. Bauer, M. Scutari and R. Nagarajan (2009).
BMC Bioinformatics, 10(Suppl 11):S14. Supplement containing the Proceedings of the 6th Annual MCBIOS Conference (Transformational Bioinformatics: Delivering Value from Genomes), Starkville (MS, USA), February 20–21, 2009.
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
- 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.