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Mattias Villani

Professor

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Works at Department of Statistics
Telephone 08-16 29 93
Email mattias.villani@stat.su.se
Visiting address Universitetsvägen 10 B, plan 7
Postal address Statistiska institutionen 106 91 Stockholm

About me

I am Professor of Statistics at Stockholm University and Linköping University.

My research focuses on developing computationally efficient Bayesian methods for inference, prediction and decision making using flexible probabilistic models.
My current application areas are machine learning, transportation, neuroimaging, econometrics and robotics.

I currently teach the Master's level courses Bayesian Statistics at Stockholm University and Bayesian Learning at Linköping University. I also teach the PhD level course Advanced Bayesian Learning.

Selected publications

  • Hamiltonian Monte Carlo with Energy Conserving Subsampling, Journal of Machine Learning Research, 2019 (with Khue-Dung Dang, Matias Quiroz, Robert Kohn and Minh Ngoc Tran)
  • Speeding Up MCMC by Efficient Data Subsampling, Journal of the American Statistical Association, 2018 (with Matias Quiroz, Robert Kohn and Minh Ngoc Tran)
  • Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models, Journal of Computational and Graphical Statistics, 2018 (with Måns Magnusson, Leif Jonsson and David Broman)
  • Tree Ensembles with Rule Structured Horseshoe Regularization, Annals of Applied Statistics, 2018  (with Malte Nalenz)
  • Fast Bayesian Whole-Brain fMRI Analysis with Spatial 3D Priors, NeuroImage, 2017 (with Per Sidén, Anders Eklund and David Bolin).
  • Generalized Smooth Finite Mixtures, Journal of Econometrics, 2012 (with Robert Kohn and David Nott).
  • Efficient Bayesian Multivariate Surface Regression, Scandinavian Journal of Statistics, 2013 (with Feng Li).
  • Steady State Priors for Vector Autoregressions, Journal of Applied Econometrics, 2009.
  • Bayesian Estimation of an Open Economy DSGE Model with Incomplete Pass-Through, Journal of International Economics, 2007 (with Malin Adolfson, Stefan Laséen and Jesper Lindé).
  • Bayesian Point Estimation of the Cointegration Space, Journal of Econometrics, 2006.
  • Bayesian Reference Analysis of Cointegration, Econometric Theory, 2005.

Last updated: November 22, 2019

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