Istituto di Matematica  Applicata
e Tecnologie Informatiche "E.Magenes" - IMATI
via Bassini 15, 20133 Milano

Raffaele Argiento


  About me


I'm Researcher at the Italian National Research Council (Consiglio Nazionale delle Ricerche), Istituto di Matematica  Applicata e Tecnologie Informatiche  "E. Magenes" (CNR-IMATI), Milan (Italy).
I have been lecturer (Academic Year 2015/16) of Statistics at  University of Kent, School of Mathematics, Statistics and Actuarial Science (SMSAS).
I completed my Ph.D in Statistics at Bocconi University, Milano. I spent part of my Ph.D studies at University of Pennsylvania (Philadelphia, USA).


My research interests are mainly focused on  Bayesian inference (parametric and nonparametric), with emphasis on computational aspects. My main activity concerns  mixture models for cluster analysis (if you are interested in some details  give a look at this page of my institute).
From the application point of view, my work concerns reliability and, more in general, probability and statistics for engineering, health care management and medicine (you can find something about that at this other page of my institute).


I taught undergraduate and post-graduate courses at Polytechnic of Milan,  Bocconi University, Bicocca University and University of Kent; a complete list of my courses is here.

I'm executive director of the Applied Bayesian Statistics School ABS; click here to visit ABS 16 web page and here for a list of past ABS schools.

I organized the third edition of BAYSM (2016), that took  place in Florence. For more information about BAYSM, follow this link.  

If you are interested, you can find below links to some paper to which I'm currently working on:

  • ``A conditional algorithm for Bayesian finite mixture models via normalized point process'', preview available as SIS proceeding.

  • ``A Hierarchical Nonparametric Approach for Robust Graphical Modelling'', to be submitted. 
  • ``A priori truncation method for posterior sampling from homogeneous normalized completely random measure mixture models'', accepted on the Electronic Journal of Statistics.
  • ``Shape clustering by nonparametrics mixutre of principal curve'', preview available as SIS proceeding.
  • ``The Energy Production Profile of a Large Number of Residential co-generations: a Statistical Evaluation'', IMATI - Technical report (click to download).

Publications Teaching  Projects
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