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- Minh Ngoc Tran
Dr Minh Ngoc Tran
Associate Investigator
The University of Sydney
Minh-Ngoc is currently senior lecturer at the Discipline of Business Analytics, University of Sydney Business School.
He received a PhD in Statistics from the National University of Singapore, a Master and a Bachelor in Mathematics from the Vietnam National University, Hanoi
Research Interests:
Monte Carlo Methods
Statistical machine learning
Qualifications:
PhD in Statistics
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).
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.
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).
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
Wall, L., Gunawan D., Brown S., Tran M N., Kohn R., & Hawkins G.
(2021). dentifying relationships between cognitive processes across tasks, contexts, and time.
Behavior Research Methods. 53(1), 78–95. doi: 10.3758/s13428-020-01405-4
Gunawan, D., Hawkins G., Tran M N., Kohn R., & Brown S.
(2020). New estimation approaches for the hierarchical linear ballistic accumulator models.
Journal of Mathematical Psychology. 96, 102368. doi: 10.1016/j.jmp.2020.102368
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
Drovandi, C. C., & Tran M N.
(2018). Improving the Efficiency of Fully Bayesian Optimal Design of Experiments Using Randomised Quasi-Monte Carlo.
Bayesian Analysis. 13(1), 139–162. doi: 10.1214/16-BA1045
Ong, V. M. H., Nott D. J., Tran M N., Sisson S., & Drovandi C. C.
(2018). Variational Bayes with synthetic likelihood.
Statistics and Computing. 28(4), 971-988. doi: 10.1007/s11222-017-9773-3
Ong, V. M. - H., Nott D. J., Tran M N., Sisson S., & Drovandi C. C.
(2018). Likelihood-free inference in high dimensions with synthetic likelihood.
Computational Statistics & Data Analysis. 128, 271 - 291. doi: 10.1016/j.csda.2018.07.008
Dang, K.. D., Quiroz M., Kohn R., Tran M N., & Villani M.
(2017). Hamiltonian Monte Carlo with Energy Conserving Subsampling.
Tran, M N., Nott D. J., & Kohn R.
(2017). Variational Bayes With Intractable Likelihood.
Journal of Computational and Graphical Statistics. 26(4), 873 - 882. doi: 10.1080/10618600.2017.1330205
Gunawan, D., Tran M N., Suzuki K.., Dick J.., & Kohn R.
(2016). Computationally efficient Bayesian estimation of high dimensional copulas with discrete and mixed margins.
Tran, M N., Pitt M. K., & Kohn R.
(2016). Adaptive Metropolis–Hastings sampling using reversible dependent mixture proposals.
Statistics and Computing. 26(1-2), 361-381. doi: 10.1007/s11222-014-9509-6
Tran, M N., Nott D., Kuk A., & Kohn R.
(2016). Parallel variational Bayes for large datasets with an application to generalized linear mixed models.
Journal of Computational and Graphical Statistics. 25(2), 626-646. doi: 10.1080/10618600.2015.1012293
Tran, M N., Giordani P., Mun X., Kohn R., & Pitt M. K.
(2014). Copula-Type Estimators for Flexible Multivariate Density Modeling Using Mixtures.
Journal of Computational and Graphical Statistics. 23(4), 1163-1178. doi: 10.1080/10618600.2013.842918
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.
Gunawan, D., Tran M N., & Kohn R.
(2016). Fast Inference for Intractable Likelihood Problems using Variational Bayes.
Gunawan, D., Tran M N., Suzuki K., Dick J., & Kohn R.
(2016). Computationally Efficient Bayesian Estimation of High Dimensional Copulas with Discrete and Mixed Margins.