Marco Scutari, Ph.D.
Lecturer in Statistics
Contact Information
Department of Statistics
University of Oxford
24–29 St. Giles'
Oxford, OX1 3LB, United Kingdom
work email: scutari@stats.ox.ac.uk
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
- Who Learns Better Bayesian Network Structures: Constraint-Based, Score-based or Hybrid Algorithms?
[ ]
M. Scutari, C. E. Graafland and J. M. Gutierrez (in preparation).
Journal of Machine Learning Research (72, Proceedings Track, PGM 2018). - Learning Bayesian Networks from Big Data with Greedy Search: Computational Complexity and Efficient Implementation.
[ arXiv (preprint) ]
M. Scutari, C. Vitolo and A. Tucker (submitted).
Statistics and Computing. - A Network Perspective of Engaging Patients in Specialist and Chronic Illness Care: the 2014 International Health Policy Survey.
[ ]
Y.-S. Chao, H.-T. Wu, M. Scutari, T.-S. Chen, C.-J. Wu, M. Durand and A. Boivin (submitted).
PLoS ONE.
- Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle.
[ arXiv (preprint) |
html |
pdf ]
M. Scutari (2018).
Behaviormetrika. This is an extended version of the “Dirichlet Bayesian Network Scores and the Maximum Entropy Principle” JMLR Proceedings paper. - Modelling Air Pollution, Climate and Health Data Using Bayesian Networks: a Case Study of the English Regions.
[ html |
pdf ]
C. Vitolo, M. Scutari, M. Ghalaieny, A. Tucker and A. Russell (2018).
Earth and Space Science. - 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).
Journal of Machine Learning Research (73, Proceedings Track, 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
- Measures of Variability for Graphical Models.
[ pdf (final + fixes) ]
M. Scutari (2011).
Ph.D. School in Statistical Sciences, University of Padova.
Working Papers & Technical Reports
- Structure Variability in Bayesian Networks.
[ arXiv |
pdf ]
M. Scutari (2009).
Working Paper 13-2009, Department of Statistical Sciences, University of Padova.
Teaching Material
My teaching material is available here.Invited Talks, (Short) Course Slides, Conference Presentations and Posters
- 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). - Genotype-Environment Effects Analysis Using Bayesian Networks.
[ pdf ]
7th International Conference of the ERCIM WG on Computational and Methodological Statistics, Pisa (December 6-8, 2014). - Predictive Accuracy: a Function of Genetic Distance.
[ pdf ]
Workshop on “Statistical and Computational Methods for Relatedness and Relationship Inference from Genetic Marker Data”, International Centre for Mathematical Sciences (ICMS), Edinburgh (September 22-26, 2014). - On the Prior and Posterior Distributions Used in Graphical Modelling.
[ pdf ]
Joint Statistical Meeting, Boston (August 5, 2014). A shorter version of the talk given in Oxford in 2013. - Multiple Quantitative Trait Analysis in Statistical Genetics with Bayesian Networks.
[ pdf ]
“Graphical Causality Models: Tree, Bayesian Networks and Big Data”, ENBIS-SFdS Spring Meeting, Institut Henri Poincareé, Paris (April 9, 2014). And again at the Department of Genetics, Evolution and Environment, University College London (June 13, 2014). And again at the 11th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB, June 28, 2014). And again at “Integrating the Genome with the Phenome”, BCGES-SEGEG Annual Meeting, London (July 8, 2014). - Graphical Models for Genomic Selection.
[ pdf ]
Unité Mathématiques et Informatique Appliquées (MIA), INRA, Jouy-en-Josas (November 7, 2013). An improved version of the MAGIC Workshop talk. - On the Prior and Posterior Distributions Used in Graphical Modelling.
[ pdf ]
Graphical Modelling Reading Club, Oxford University (October 25, 2013). - Bayesian Network Modelling in Genetics and Systems Biology.
[ pdf ]
Biomathematics Seminar, Imperial College, London (October 15, 2013). An improved version of Liverpool's Computational Biology Seminar talk. - Applications of Bayesian Networks in Genetics and Systems Biology.
[ pdf ]
Computational Biology Seminar, University of Liverpool (September 13, 2013). - Learning Bayesian Networks in R: an Example in Systems Biology.
[ pdf ]
useR! Conference, Albacete (July 9, 2013). - Graphical Models for Genomic Selection.
[ pdf ]
MAGIC Workshop, National Institute of Agricultural Botany (NIAB), Cambridge (June 12, 2013). - Bayesian Networks for Gene Network Discovery: Parallel and Optimised Learning.
[ pdf ]
Mathematical and Statistical Aspects of Molecular Biology Conference (MASAMB'13), Imperial College, London (April 11-12, 2013). - Causal Protein Signalling Networks.
[ pdf ]
Department of Genetics, Evolution and Environment, University College London (January 25, 2013). - Graphical Models and Protein Signalling Networks.
[ pdf ]
Astellas, Leiden (November 5, 2012). - Graphical Modelling in Genetics and Systems Biology.
[ pdf ]
Workshop on the Foundations of Biomedical Knowledge Representation, Leiden (October 30, 2012). - Efficient Use of Marker Profiles in Genomic Selection.
[ pdf ]
15th Meeting of the EUCARPIA Section “Biometrics in Plant Breeding”, Hohenheim (September 5, 2012). - Genomic Selection with Linear Models and Rank Aggregation.
[ pdf ]
Genetics Institute, University College London (March 5, 2012). - Graphical Models: Model Estimation and Validation.
[ pdf ]
Department of Statistical Sciences, University of Padova (September 27, 2011). - On Identifying Significant Edges in Graphical Models.
[ pdf ]
Workshop on Probabilistic Problem Solving in Biomedicine, 13th Conference on Artificial Intelligence in Medicine (AIME'11), Bled (July 2, 2011). - Measures of Variability for Graphical Models.
[ pdf ]
Genetics Institute, University College London (March 14, 2011). - Bayesian Network Resampling for the Analysis of Functional Relationships.
[ pdf ]
Institut für Medizinische Informatik, Statistik und Epidemiologie (IMISE), Leipzig University (October 12, 2010). - Constraint-based Bayesian Network Learning with Permutation Tests.
[ pdf ]
Statistics for Complex Problems: the Multivariate Permutation Approach and Related Topics, Archivio Antico, Palazzo del Bo, University of Padova (June 15, 2010). - Structure Variability in Graphical Models.
[ pdf ]
Machine Learning / Intelligent Data Analysis Group, Institut für Softwaretechnik und Theoretische Informatik, Technische Universität Berlin (November 5, 2009). - Comparing Bayesian Networks and Structure Learning Algorithms.
[ pdf ]
Institut für Medizinische Informatik, Statistik und Epidemiologie (IMISE), Leipzig University (October 20, 2009). - Network Bayesiani: Selezione del Modello. (in Italian)
[ pdf ]
Department of Information Engineering, University of Padova (November 4, 2008).