Dr Matias Quiroz
Associate Investigator
I received my Ph.D. degree from the Department of Statistics at Stockholm University 2015, under the supervision of Professor Mattias Villani. I also hold a M.Sc. degree in Engineering Mathematics from Lund University (2009). I joined ACEMS in 2017, where I worked under the supervision of CI Professor Robert Kohn until 2019. I am currently a Lecturer in Statistics at the University of Technology Sydney (UTS).
My research interests lie in the area of Bayesian Statistics. In particular, I am interested in computationally challenging problems, especially in Markov chain Monte Carlo simulation algorithms and variational inference.
Research Interests:
Big Data
Computational statistics
Monte Carlo Methods
Qualifications:
Ph. D. in Statistics, Stockholm University, 2015
M. Sc. in Engineering Mathematics, Lund University, 2009
Projects
Publications
Invited talks, refereed proceedings and other conference outputs
(2019). Variance reduction properties of the reparameterisation trick.
(Chaudhuri, K., & Sugiyama M., Ed.).The 22nd International Conference on Artificial Intelligence and Statistics. 89, 2711-2720.
(2018). The Mathematics of Biological Systems Management Symposium.
The Mathematics of Biological Systems Management Symposium.
(2018). Hamiltonian Monte Carlo with energy conserving subsampling.
The 2nd International Conference on Econometrics and Statistics (EcoSta 2018).
(2018). Gaussian variational approximation for high-dimensional state space models.
2nd International Conference on Econometrics and Statistics.
(2018). The block-Poisson estimator for optimally exact subsampling MCMC.
International Society of Bayesian Analysis.
(2017). Exact Subsampling MCMC.
1st International Conference on Econometrics and Statistics (EcoSta 2017).
(2017). Hamiltonian Monte Carlo with Energy Conserving Subsampling.
Bayes on the Beach.
(2017). The block pseudo-marginal sampler.
Bayes on the beach.
(2017). Gaussian variational approximation for high-dimensional state space models.
The Sydney Time Series & Forecasting Symposium.
(2017). The block-pseudo marginal sampler.
The 1st International Conference on Econometrics and Statistics (EcoSta 2017).
Journal Articles
(2019). Speeding Up MCMC by Efficient Data Subsampling.
Journal of the American Statistical Association. 114(526), 831-843. doi: 10.1080/01621459.2018.1448827
(2017). Hamiltonian Monte Carlo with Energy Conserving Subsampling.
Technical reports and unrefereed outputs
(2016). Block-Wise Pseudo-Marginal Metropolis-Hastings.
(2016). Exact Subsampling MCMC.
