Tutor in Statistics at Somerville College
Marco studied Statistics and Computer Science at the University of Padova, Italy. He earned his PhD in Statistics in Padova under the guidance of Professor A. Brogini and Professor K. Strimmer, with a dissertation on prior and posterior distributions used in graphical modelling. In 2011, he moved to University College London (UCL) as a Research Associate in Statistical Genetics at the Genetics Institute (UGI), later joining the Department of Statistics in Oxford as a Lecturer in Statistics in 2014. His research focuses on the theory of Bayesian networks and their applications, often to biological data. He is the author and maintainer of the bnlearn R package, and wrote “Bayesian Networks in R with Applications in Systems Biology” for Springer and “Bayesian Networks with Examples in R” for Chapman and Hall.
- Graphical models (Bayesian networks in particular)
- Machine learning
- Statistical genetics
- Systems biology
The main focus of my research is Bayesian networks, approaching graphical modelling from a machine learning perspective. From my time at UGI, I often apply Bayesian networks to genetics and systems biology, but I am also interested in and have worked on applications in other fields (environmental data, clinical studies etc.). My interests on this topic range from the theoretical (model selection, estimation and validation), to the applied (tailoring existing theory to specific applications), to scientific computing (implementing stuff in my bnlearn R package and making it scale).
- M. Scutari, P. Auconi, G. Caldarelli and L. Franchi (2017). Bayesian Networks Analysis of Malocclusion Data. Scientific Reports.
- M. Scutari (2016). An Empirical-Bayes Score for Discrete Bayesian Networks. Journal of Machine Learning Research, 52, 438–448.
- M. Scutari (2013). On the Prior and Posterior Distributions Used in Graphical Modelling (with discussion). Bayesian Analysis, 8(3), 505–532.
- M. Scutari, P. Howell, D. J. Balding and I. Mackay (2014). Multiple Quantitative Trait Analysis Using Bayesian Networks. Genetics, 198(1), 129–137.
- M. Scutari (2017).Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Package. Journal of Statistical Software, 77(2):1–20.
- R. Nagarajan, M. Scutari and S. Lèbre (2013). Bayesian Networks in R with Applications in Systems Biology. Use R!, Springer (US).
- M. Scutari and J.-B. Denis (2014). Bayesian Networks with Examples in R. Chapman & Hall.
- M. Scutari (2010). Learning Bayesian Networks with the bnlearn R Package. Journal of Statistical Software, 35(3), 1–22.