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Professor Mattias Villani
Professor, Professor
Linköping University
Mattias Villani is Professor of Statistics Stockholm University and Linköping University, Sweden. Mattias Villani research focuses on developing computationally efficient Bayesian methods for inference, prediction and decision making using flexible probabilistic models. His current application areas are spatiotemporal modeling, neuroimaging, transportation, and econometrics.
Research Interests:
Bayesian computational methods
Bayesian Econometrics
Bayesian methodolgy
Markov Chain Monte Carlo
Mixture models
Neuroimaging
Nonparametric regression
Predictive Models
robotics and autonomous systems
Text analysis
Projects
Publications
Invited talks, refereed proceedings and other conference outputs
Quiroz, M., Tran M N., Villani M., Kohn R., & Dang K-D.
(2018). The block-Poisson estimator for optimally exact subsampling MCMC.
International Society of Bayesian Analysis.
Dang, K.. D., Quiroz M., Kohn R., Tran M N., & Villani M.
(2018). Hamiltonian Monte Carlo with energy conserving subsampling.
The 2nd International Conference on Econometrics and Statistics (EcoSta 2018).
Tran, M N., Kohn R., Quiroz M., & Villani M.
(2017). The block pseudo-marginal sampler.
Bayes on the beach.
Quiroz, M., Tran M N., Villani M., & Kohn R.
(2017). Exact Subsampling MCMC.
1st International Conference on Econometrics and Statistics (EcoSta 2017).
Dang, K.-D., Quiroz M., Kohn R., Tran M N., & Villani M.
(2017). Hamiltonian Monte Carlo with Energy Conserving Subsampling.
Bayes on the Beach.
Tran, M N., Kohn R., Quiroz M., & Villani M.
(2017). The block-pseudo marginal sampler.
The 1st International Conference on Econometrics and Statistics (EcoSta 2017).
Journal Articles
Quiroz, M., Kohn R., Villani M., & Tran M N.
(2019). Speeding Up MCMC by Efficient Data Subsampling.
Journal of the American Statistical Association. 114(526), 831-843. doi: 10.1080/01621459.2018.1448827
Sidén, P., Eklund A., Bolin D., & Villani M.
(2017). Fast Bayesian whole-brain fMRI analysis with spatial 3D priors.
NeuroImage. 146, 211–225. doi: 10.1016/j.neuroimage.2016.11.040
Dang, K.. D., Quiroz M., Kohn R., Tran M N., & Villani M.
(2017). Hamiltonian Monte Carlo with Energy Conserving Subsampling.
Eklund, A., Lindquist M. A., & Villani M.
(2017). A Bayesian heteroscedastic GLM with application to fMRI data with motion spikes.
NeuroImage. 155, 354–369. doi: 10.1016/j.neuroimage.2017.04.069
Magnusson, M., Jonsson L., Villani M., & Broman D.
(2017). Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models.
Journal of Computational and Graphical Statistics. doi: 10.1080/10618600.2017.1366913
Technical reports and unrefereed outputs
Dang, K.-D., Quiroz M., Kohn R., Tran M N., & Villani M.
(2017). Hamiltonian Monte Carlo with Energy Conserving Subsampling.
Tran, M N., Kohn R., Quiroz M., & Villani M.
(2016). Block-Wise Pseudo-Marginal Metropolis-Hastings.
Quiroz, M., Villani M., & Kohn R.
(2016). Exact Subsampling MCMC.