Minh-Ngoc Tran

Photo of Minh-Ngoc Tran


Senior Lecturer

Rm 4091
H70 - Abercrombie Building
The University of Sydney
NSW 2006 Australia

Telephone +61 2 8627 4752
minh-ngoc.tran@sydney.edu.au
Web Minh-Ngoc Tran's site

Bio

Minh-Ngoc’s main research interests lie in Bayesian methodology and statistical machine learning. He specialises in fast Variational Bayes and simulation-based methods, such as importance sampling and sequential Monte Carlo, for estimating complex models with Big Data, and in Lasso-type variable selection methods.

His current research is focused on developing efficient methods for estimating statistical models with an intractable likelihood, of which Big Data problems and Approximate Bayesian Computation are special cases.

Minh Ngoc received a PhD in Statistics from the National University of Singapore, a Master and a Bachelor in Mathematics from the Vietnam National University, Hanoi. Before joining the University of Sydney, he worked as a postdoctoral fellow at the University of New South Wales. He is an Associate Investigator in the ARC’s Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS).

Selected publications

2018

Journal Articles

Drovandi C, and Tran M (2018) Improving the Efficiency of Fully Bayesian Optimal Design of Experiments Using Randomised Quasi-Monte Carlo Bayesian Analysis, 13 (1), 139-162. [More Information]

Gunawan D, Tran M, Suzuki K, Dick J, and Kohn R (2018) Computationally Efficient Bayesian Estimation of High Dimensional Archimedian Copulas with Discrete and Mixed Margins Statistics and Computing, In Press.

Ong V, Nott D, Tran M, Sisson S, and Drovandi C (2018) Likelihood-free inference in high dimensions with synthetic likelihood Computational Statistics and Data Analysis, 128, 271-291. [More Information]

Ong V, Nott D, Tran M, Sisson S, and Drovandi C (2018) Variational Bayes with Synthetic Likelihood Statistics and Computing, 28 (4), 971-988. [More Information]

Quiroz M, Tran M, 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. [More Information]

Quiroz M, Villani M, Kohn R, and Tran M (2018) Speeding up MCMC by efficient data subsampling Journal of the American Statistical Association, In Press. [More Information]

Tung D, Tran M, and Cuong T (2018) Bayesian adaptive lasso with variational Bayes for variable selection in high-dimensional generalized linear mixed models Communications in Statistics: Simulation and Computation, In Press. [More Information]

Villani M, Quiroz M, Kohn R, Tran M, and Dang K (2018) Subsampling MCMC - A review for the survey statistician Sankhya. Series A: mathematical statistics and probability, In Press.

2017

Journal Article

Tran M, Nott D, and Kohn R (2017) Variational Bayes with Intractable Likelihood Journal of Computational and Graphical Statistics, 26 (4), 873-882. [More Information]

2016

Journal Articles

Tran M, Nott D, Kuk A, and 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. [More Information]

Tran M, Pitt M, and Kohn R (2016) Adaptive Metropolis-Hastings sampling using reversible dependent mixture proposals Statistics and Computing, 26 (1), 361-381. [More Information]

2015

Conference Proceeding

Tran M, Nott D, and Kohn R (2015) Variational Bayes with Intractable Likelihood 5th Vietnam National Congress in Probability and Statistics; Vietnam Institute for Advanced Study in Mathematics, Da Nang, Vietnam.

2014

Journal Articles

Leng C, Tran M, and Nott D (2014) Bayesian adaptive Lasso Annals of the Institute of Statistical Mathematics, 66 (2), 221-244. [More Information]

Tran M, Giordani P, Mun X, Kohn R, and Pitt M (2014) Copula-Type Estimators for Flexible Multivariate Density Modeling Using Mixtures Journal of Computational and Graphical Statistics, 23 (4), 1163-1178. [More Information]

2013

Journal Articles

Giordani P, Mun X, Tran M, and Kohn R (2013) Flexible Multivariate Density Estimation with Marginal Adaptation Journal of Computational and Graphical Statistics, 22 (4), 814-829. [More Information]

Zhang W, Xiaoxia C, and Tran M (2013) The structural features and the deliberative quality of online discussions Telematics and Informatics, 30 (2), 74-86. [More Information]

Seminar Paper

Tran M (2013) Adaptive Metropolis-Hastings sampling using reversible dependent mixture proposals 8th Vietnamese Mathematical Conference; Vietnam Institute for Advanced Study in Mathematics, Nha Trang, Vietnam.

2012

Journal Articles

Nott D, Marshall L, and Tran M (2012) The ensemble Kalman filter is an ABC algorithm Statistics and Computing, 22 (6), 1273-1276. [More Information]

Nott D, Tran M, and Leng C (2012) Variational approximation for heteroscedastic linear models and matching pursuit algorithms Statistics and Computing, 22 (2), 497-512. [More Information]

Tran M, and Nott D (2012) Simultaneous variable selection and component selection for regression density estimation with mixtures of heteroscedastic experts Electronic Journal of Statistics, 6, 1170-1199. [More Information]

Tran M, Giordani P, and Kohn R (2012) Discussion of "Fast sparse regression and classification" by Jerome Friedman International Journal of Forecasting, 28 (3), 749-750. [More Information]

Tran M, Giordani P, and Kohn R (2012) Discussion of “Fast sparse regression and classification�? by Jerome Friedman International Journal of Forecasting, 28 (3), 749-750. [More Information]

Tran M, Nott D, and Leng C (2012) The predictive Lasso Statistics and Computing, 22 (5), 1069-1084. [More Information]

2011

Journal Articles

Tran M (2011) The loss rank criterion for variable selection in linear regression analysis Scandinavian Journal of Statistics: theory and applications, 38 (3), 466-479. [More Information]

Tran M (2011) A criterion for optimal predictive model selection Communications in Statistics - Theory and Methods, 40 (5), 893-906. [More Information]

2010

Journal Article

Hutter M, and Tran M (2010) Model selection with the Loss Rank Principle Computational Statistics and Data Analysis, 54 (5), 1288-1306. [More Information]

2009

Journal Article

Tran M (2009) Penalized Maximum Likelihood Principle for Choosing Ridge Parameter Communications in Statistics: Simulation and Computation, 38 (8), 1610-1624. [More Information]

Recent Units Taught

  • BUSS1020 Quantitative Business Analysis

  • BUSS6002 Data Science in Business

  • BUSS7904 Advanced Analysis for Research

  • QBUS3820 Data Mining and Data Analysis

  • QBUS3830 Advanced Analytics

  • QBUS5001 Quantitative Methods for Business