Research


Pipeline

  • Subsampling Sequential Monte Carlo for Static Bayesian Models (with David Gunawan, Robert Kohn, Khue-Dung Dang and Minh-Ngoc Tran). ArXiv
  • Gaussian Variational Approximation for High-dimensional State Space Models (with David Nott and Robert Kohn). ArXiv
  • Hamiltonian Monte Carlo with Energy Conserving Subsampling (with Khue-Dung Dang, Robert Kohn, Minh-Ngoc Tran and Mattias Villani). ArXiv
  • The Block-Poisson Estimator for Optimally Tuned Exact Subsampling MCMC (with Minh-Ngoc Tran, Mattias Villani, Robert Kohn and Khue-Dung Dang). ArXiv
  • The Block Pseudo-Marginal Sampler (with Minh-Ngoc Tran, Robert Kohn and Mattias Villani). ArXiv
  • Dynamic Mixture-of-Experts Models for Longitudinal and Discrete-Time Survival Data (with Mattias Villani). SSRN. Revised paper here.

Peer-reviewed Publications

  • Xu, M., Quiroz, M., Kohn, R. and Sisson, S. A. (2019). Variance Reduction Properties of the Reparameterization Trick. To appear in AISTATS 2019. ArXiv. Poster.
  • Quiroz, M., Villani, M., Kohn, R., Tran, M-N and Dang, K-D. (2018). Subsampling MCMC – An Introduction for the Survey Statistician. To appear in Sankhya A. JournalArXiv.
  • Quiroz, M., Kohn, R., Villani, M. and Tran, M-N.   (2018). Speeding up MCMC by Efficient Data Subsampling. To appear in Journal of the American Statistical Association. Journal. ArXiv.
  • Quiroz, M., Tran, M-N., Villani, M. and Kohn, R. (2018). Speeding up MCMC by Delayed Acceptance and Data Subsampling. Journal of Computational and Graphical Statistics, 27(1):12-22. JournalArXiv.

Other Publications

  • Bayesian Inference in Large Data Problems. Doctoral Thesis. DiVA