Bayesian Nonparametrics for complex data

"Bayesian Nonparametrics for complex data" is a research project held at the Department of Statistical Sciences of the University of Padova from March 2018 to February 2020. The research project aimed at investigating new approaches for modelling high complexity data which arise in many modern applied contexts by means of Bayesian nonparametric and semiparametric tools. The Principal Investigator is Antonio Canale.

Project's concluding workshop

24 January 2020, 9.00 - 17.00 More information

Research team

Antonio Canale (PI)

Assistant professor of Statistics

Marco Stefanucci

Postdoctoral fellow

Jan van Waaij

Postdoctoral fellow

Laura D'Angelo

PhD Student

Lorenzo Schiavon

PhD Student

Jacopo Schiavon

PhD Student


Papers in peer reviewed journals

Under review

  • Chandra, Canale, Dunson, Escaping the curse of dimensionality in Bayesian model-based clustering
  • Zorzetto, Canale, Marani, Bayesian non-asymptotic extreme value models for environmental data
  • Stefanucci, Canale, Multiscale stick-breaking mixture models
  • Canale, Corradin, Nipoti, Importance conditional sampling for Pitman-Yor mixtures
  • van Waaij, Adaptive posterior contraction rates for empirical Bayesian drift estimation of a diffusion
  • van Waaij, Kleijn, Recovery, detection and confidence sets of communities in a sparse stochastic block model
  • van Waaij, Kleijn, Uncertainty quantification in the stochastic block model with an unknown number of classes
  • Canale, Lijoi, Pruenster, Nipoti, Normalized random measures with spike and slab expectation
  • Bernardi, Canale, Stefanucci, Locally sparse function-on-function regression

Supporting Talents in Research

"Bayesian Nonparametrics for complex data" is funded under the "Supporting Talents in Research" (STARS@Unipd) initiative.