Find us on Facebook Find us on LinkedIn Follow us on Twitter Subscribe to our YouTube channel Instagram

Minh-Ngoc Tran

Minh-Ngoc Tran


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

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).

2017

4
Journal Article/s

Tran M, Nott D and Kohn R 2017 Forthcoming 'Variational Bayes with Intractable Likelihood', Journal of Computational and Graphical Statistics [Link]

Drovandi C and Tran M 2017 Forthcoming 'Improving the Efficiency of Fully Bayesian Optimal Design of Experiments using Randomised Quasi-Monte Carlo', Bayesian Analysis [Link]

Quiroz M, Tran M, Villani M and Kohn R 2017 Forthcoming 'Speeding up MCMC by delayed acceptance and data subsampling', Journal of Computational and Graphical Statistics [Link]

Ong VMH, Nott DJ, Tran M, Sisson SA and Drovandi CC 2017 Forthcoming 'Variational Bayes with Synthetic Likelihood', Statistics and Computing

2016

4
Journal Article/s

Tran M, Pitt M and Kohn R 2016 'Adaptive Metropolis-Hastings sampling using reversible dependent mixture proposals', Statistics and Computing, vol.26:1, pp. 361-81 [Link]

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, vol.25:2, pp. 626-46 [Link]

2015

5
Conference Paper/s

Tran M, Nott D and Kohn R 2015 'Variational Bayes with Intractable Likelihood', 9th Conference of the Asian Regional Section of the International Association for Statistical Computing IASC-ARS 2015, Singapore, 19th December 2015

Tran M and Kohn R 2015 'Fast estimation for copula models by Variational Bayes', 8th International Conference of the ERCIM WG on Computational and Methodological Statistics CMStatistics 2015, London, United Kingdom, 14th December 2015

Tran M, Nott D and Kohn R 2015 'Variational Bayes with Intractable Likelihood', 5th Vietnam National Conference on Probability and Statistics, Da Nang, Vietnam, 25th May 2015

2014

4
Journal Article/s

Leng C, Tran M and Nott D 2014 'Bayesian adaptive Lasso', Annals of the Institute of Statistical Mathematics, vol.66, pp. 221-44 [Link]

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, vol.23:4, pp. 1163-78 [Link]

2013

4
Journal Article/s

Giordani P, Mun X, Tran M and Kohn R 2013 'Flexible Multivariate Density Estimation With Marginal Adaptation', Journal of Computational and Graphical Statistics, vol.22:4, pp. 814-29 [Link]

5
Conference Paper/s

Nott D, Tran M and Leng C 2013 'Variable selection in high dimensional heteroscedastic linear regression', 9th International Chinese Statistical Association Conference, Hong Kong, (China), 23rd December 2013

Tran M, Pitt M and Kohn R 2013 'Adaptive Metropolis-Hastings sampling', 8th Vietnamese Mathematical Congress, Nha Trang, Vietnam, 14th August 2013

2012

4
Journal Article/s

Tran M, Giordani P and Kohn R 2012 'Discussion of “Fast sparse regression and classification” by Jerome Friedman', International Journal of Forecasting, vol.28:3, pp. 749-50 [Link]

Tran M, Nott D and Kohn R 2012 'Simultaneous variable selection and component selection for regression density estimation with mixtures of heteroscedastic experts', Electronic Journal of Statistics, vol.6, pp. 1170-99 [Link]

Nott D, Marshall L and Tran M 2012 'The ensemble Kalman filter is an ABC algorithm', Statistics and Computing, vol.22:6 (Special Issue on Approximate Bayesian Computation), pp. 1273-76 [Link]

Tran M, Nott D and Leng C 2012 'The predictive Lasso', Statistics and Computing, vol.22:5, pp. 1069-84 [Link]

Zhang W, Cao X and Tran M 2012 'The structural features and the deliberative quality of online discussions', Telematics and Informatics, vol.30:2, pp. 74-86 [Link]

Nott D, Tran M and Leng C 2012 'Variational approximation for heteroscedastic linear models and matching pursuit algorithms', Statistics and Computing, vol.22:2, pp. 497-512 [Link]

5
Conference Paper/s

Tran M, Nott D and Kohn R 2012 'Simultaneous variable selection and component selection for regression density estimation with mixtures of heteroscedastic experts', ISBA World Meeting 2012, Kyoto, Japan, 29th June 2012

2011

4
Journal Article/s

Tran M 2011 'A Criterion for Optimal Predictive Model Selection', Communications in Statistics - Theory and Methods, vol.40:5, pp. 893-906 [Link]

Tran M 2011 'The Loss Rank Criterion for Variable Selection in Linear Regression Analysis', Scandinavian Journal of Statistics, vol.38:3, pp. 466–79 [Link]

2010

4
Journal Article/s

Hutter M and Tran M 2010 'Model selection with the Loss Rank Principle', Computational Statistics & Data Analysis, vol.54:5, pp. 1288–306 [Link]

5
Conference Paper/s

Tran M, Nott D and Leng C 2010 'The predictive lasso', First Singapore Conference on Statistical Science, Singapore, 19th November 2010

Tran M 2010 'Criteria for Model Identification and Optimal Predictive Model Selection', 73 IMS Annual Meeting, Gothenburg, Sweden, 13th August 2010

2009

4
Journal Article/s

Tran M 2009 'Penalized Maximum Likelihood Principle for Choosing Ridge Parameter', Communications in Statistics - Simulation and Computation, vol.38:8, pp. 1610-24 [Link]

5
Conference Paper/s

Tran M 2009 'The loss rank criterion for variable selection', 5th Mathematics and Physical Sciences Graduate Congress, Bangkok, Thailand, 9th December 2009

2006

4
Journal Article/s

Huu N, Hoang V and Tran M 2006 'Central limit theorems for functional of jump Markov processes', Vietnam Journal of Mathematics, vol.33:4, pp. 443-61

Selected grants

Research Expertise

  • Statistical inference with intractable likelihood and big data
  • Variational Bayes
  • Bayesian statistics
  • Monte Carlo methods
  • Statistical machine learning
  • Copula modelling and flexible modelling

Recent Units Taught

  • BUSS1020 Quantitative Business Analysis

    2016: S1,
    2015: S2,

  • BUSS5020 Business Insights

    2017: S2,

  • BUSS6002 Data Science in Business

    2017: S1,

  • BUSS7904 Advanced Analysis for Research

    2017: S2,

  • QBUS3820 Data Mining and Data Analysis

    2017: S2,
    2016: S2,

  • QBUS3830 Advanced Analytics

    2017: S1,
    2016: S1,