photo

Marco Scutari
Ph.D. student at the Ph.D. School in Statistical Sciences
Department of Statistical Sciences
University of Padova

Contact Information
Dipartimento di Scienze Statistiche
Via Cesare Battisti 241
35121 Padova - ITALY
work email: marco.scutari@stat.unipd.it
personal email: marco.scutari@gmail.com

Here is my curriculum vitae.

Publications

Books

  • Graphical Models in R.
    R. Nagarajan, S. Datta and M. Scutari (in preparation).
    Springer.

Book Chapters

  • Introduction to Graphical Modelling. [ arXiv (preprint) ]
    M. Scutari and K. Strimmer (2010, forthcoming).
    in Handbook of Statistical Systems Biology, D. J. Balding, M. Stumpf and M. Girolami (editors), Wiley.

Refereed Journal Articles

  • Measures of Variability for Bayesian Network Graphical Structures. [ arXiv (preprint) ]
    M. Scutari (2010, submitted).
    Journal of Multivariate Analysis.
  • Functional Relationships Between Genes Associated with Differentiation Potential of Aged Myogenic Progenitors. [ html ]
    R. Nagarajan, S. Datta, M. Scutari, M. L. Beggs, G. T. Nolen and C. A. Peterson (2010).
    Frontiers in Systems Biology.
  • Learning Bayesian Networks with the bnlearn R Package. [ arXiv (preprint) | html | pdf ]
    M. Scutari (2010).
    Journal of Statistical Software, 35(3), 1–22.

Refereed Conference Proceedings

  • 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.

Working Papers & Technical Reports

  • Structure Variability in Bayesian Networks. [ arXiv ]
    M. Scutari (2009).
    Working Paper 13-2009, Department of Statistical Sciences, University of Padova.

Invited Speaks and Conference Presentations

  • Constraint-based Bayesian Network Learning with Permutation Tests.
    Statistics for Complex Problems: the Multivariate Permutation Approach and Related Topics,
    Archivio Antico, Palazzo del Bo, University of Padova (June 15, 2010). [ pdf ]
  • Structure Variability in Graphical Models.
    Machine Learning / Intelligent Data Analysis Group, Institut für Softwaretechnik und Theoretische Informatik,
    Technische Universität Berlin (November 5, 2009). [ pdf ]
  • Comparing Bayesian networks and structure learning algorithms.
    Institut für Medizinische Informatik, Statistik und Epidemiologie (IMISE),
    Leipzig University (October 20, 2009). [ pdf ]
  • Network Bayesiani: selezione del modello. (in Italian)
    Department of Information Engineering,
    University of Padova (November 4, 2008). [ pdf ]