Minh Ngoc Tran | ARC Centre of Excellence for Mathematical and Statistical Frontiers

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

Developing flexible copula based methods

A copula is a multivariate model whose marginals are uniform. A copula based model is one that is based on a copula. The attraction of using copula models is that we can separately model the marginal distributions and the dependence structure of our target distribution. Such models are particularly attractive when some or all of the marginals are discrete or a mixture of discrete and continuous components. A multivariate probit model is one simple example of a copula model.

Enabling Bayesian inference for big data

In the last decade or so, there has been a dramatic increase in storage facilities and the possibility of processing huge amounts of data. This has made large high-quality data sets widely accessible for practitioners. This technology innovation seriously challenges inference methodology, in particular simulation algorithms commonly applied in Bayesian inference. These algorithms typically require repeated evaluations over the whole data set when fitting models, precluding their use in the age of so called big data.

Flexible models and methods for cognitive model-based decision making

This project pursues breakthroughs which allow important questions of basic and applied science to be addressed using mathematical decision models. Advances are made through an interdisciplinary effort, combining recent developments in econometric and statistical methods, cognitive science, and advances in computing. The outcomes will bring to a new range of questions a proven and powerful approach for investigating psychological effects. The project will begin this expansion effort with investigations of choices about health care and consumer purchases.

Hamiltonian Monte Carlo with Energy Conserving Subsampling

ACEMS researchers found a way to make Hamiltonian Monte Carlo (HMC) compatible with data subsampling without deteriorating the sampling efficiency of an algorithm.

Publications

Invited talks, refereed proceedings and other conference outputs

2018

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).

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).

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).

Journal Articles

2021

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

2020

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

2019

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

2018

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

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

2017

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

Dang, K.. D., Quiroz M., Kohn R., Tran M N., & Villani M. (2017). Hamiltonian Monte Carlo with Energy Conserving Subsampling.

2016

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

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

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.

Research links

ORCID

Contact

minh-ngoc.tran@sydney.edu.au

Welcome to the ACEMS Reportal

All contributors should enter data on all of the Centre’s reportable key performance indicators (KPIs).

Dr Minh Ngoc Tran

Projects

Developing flexible copula based methods

Enabling Bayesian inference for big data

Flexible models and methods for cognitive model-based decision making

Hamiltonian Monte Carlo with Energy Conserving Subsampling

Publications

2018

2017

2021

2020

2019

2018

2017

2016

2014

2017

2016

Research links

Contact

Links

ARC Centre of Excellence for Mathematical & Statistical Frontiers

Welcome to the ACEMS Reportal

All contributors should enter data on all of the Centre’s reportable key performance indicators (KPIs).

Dr Minh Ngoc Tran

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Links

ARC Centre of Excellence for Mathematical & Statistical Frontiers