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- Robert Kohn
Professor Robert Kohn
UNSW Sydney
Research Interests:
Bayesian methodolgy
Bayesian nonparametric methods
Markov chain Monte Carlo simulation algorithms
Model averaging
Multivariate Gaussian and non-Gaussian regression
Nonparametric regression
Time series modelling
Variable selection
Projects
Prizes, awards and special recognition
2019
Outreach Participantion Award was awarded to Boris Beranger, David Gunawan, Matias Quiroz, Xuhui Fan, Jaslene Lin, Yu Yang, KD Dang, Tom Whitaker, Nick Nguyen, Mingda Xu, Igor Balnozan, Hung Dao, Vincent Chin, Scott Sisson, Robert Kohn. Awarded from the ACEMS.
Publications
Invited talks, refereed proceedings and other conference outputs
Botha, I., Drovandi C., South L., & Kohn R.
(2020). Tuning the number of state particles in exact-approximate SMC.
Bayesian Young Statisticians Meeting: Online (BAYSM:O).
Salomone, R., Quiroz M., Villani M., Kohn R., & Tran M-N.
(2020). Spectral Subsampling MCMC for Stationary Time Series.
International Conference of Machine Learning. doi: https://proceedings.icml.cc/static/paper_files/icml/2020/6077-Paper.pdf
Nguyen, N., Tran M-N., Kohn R., & Nott D.
(2019). Stochastic Variational Bayes with Particle Filter.
The 12th International Conference on Monte Carlo Methods and Applications.
Botha, I., Kohn R., & Drovandi C.
(2019). Particle Methods for Stochastic Differential Equation Mixed Effects Models.
Bayes on the Beach 2019.
Xu, M.., Quiroz M., Kohn R., & Sisson S.
(2019). Variance reduction properties of the reparameterisation trick.
(Chaudhuri, K., & Sugiyama M., Ed.).The 22nd International Conference on Artificial Intelligence and Statistics. 89, 2711-2720.
Nguyen, N., Gunawan D., Tran M-N., & Kohn R.
(2019). Long short term memory stochastic volatility.
The 3rd International Conference on Econometrics and Statistics (EcoSta 2019).
Botha, I., Kohn R., & Drovandi C.
(2019). Bayesian Parameter Inference for Stochastic Differential Equation Mixed Effects Models.
The 12th International Conference on Monte Carlo Methods and Applications.
Gunawan, D., Dang K-D., Quiroz M., Kohn R., & Tran M-N.
(2019). Subsampling Sequential Monte Carlo for Static Bayesian Models.
The 12th International Conference on Monte Carlo Methods and Applications.
Dang, K-D., Quiroz M., Kohn R., Tran M-N., & Villani M.
(2019). Efficient Bayesian inference for large data sets by HMC with energy conserving subsampling.
The 10th European Seminar on Bayesian Econometrics.
Quiroz, M., Nott D., & Kohn R.
(2018). The Mathematics of Biological Systems Management Symposium.
The Mathematics of Biological Systems Management Symposium.
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).
Quiroz, M., Nott D., & Kohn R.
(2018). Gaussian variational approximation for high-dimensional state space models.
2nd International Conference on Econometrics and Statistics.
Gunawan, D.., Carter C.., & Kohn R.
(2018). Efficiently combining Pseudo Marginal and Particle Gibbs sampling.
Joint Statistical Meeting 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.
Quiroz, M., Nott D., & Kohn R.
(2017). Gaussian variational approximation for high-dimensional state space models.
The Sydney Time Series & Forecasting Symposium.
Quiroz, M., Tran M N., Villani M., & Kohn R.
(2017). Exact Subsampling MCMC.
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.
The 1st International Conference on Econometrics and Statistics (EcoSta 2017).
Tran, M N., Kohn R., Quiroz M., & Villani M.
(2017). The block pseudo-marginal sampler.
Bayes on the beach.
Kohn, R.
(2016). Speeding Up MCMC by Efficient Data Subsampling.
ISBA 2016 World Meeting, Sardinia .
Journal Articles
Chin, V.., Gunawan D.., Fiebig D.., Kohn R., & Sisson S.
(In Press). Efficient data augmentation for multivariate probit models with panel data: An application to general practitioner decision-making about contraceptives.
Journal of Royal Statistical Society. doi: 10.1111/rssc.12393
Tran, M-N., Nguyen N., Nott D., & Kohn R.
(In Press). Bayesian Deep Net GLM and GLMM.
Journal of Computational and Graphical Statistics. doi: 10.1080/10618600.2019.1637747
Mendes, E.. F., Carter C.. K., Gunawan D.., & Kohn R.
(In Press). A Flexible particle Markov chain Monte Carlo Method.
Statistics and Computing. doi: 10.1007/s11222-019-09916-7
Gunawan, D., Khaled M. A., & Kohn R.
(2020). Mixed Marginal Copula Modeling.
Journal of Business & Economic Statistics. 38(1), 137-147. doi: 10.1080/07350015.2018.1469998
Botha, I., Kohn R., & Drovandi C.
(2020). Particle Methods for Stochastic Differential Equation Mixed Effects Models.
Bayesian Analysis. doi: 10.1214/20-BA1216,10.1214/20-BA1216SUPP
Gunawan, D.., Khaled M.. K., & Kohn R.
(2020). Mixed Marginal Copula Modeling.
Journal of Business and Economics Statistics. 38(1), 137-147. doi: 10.1080/07350015.2018.1469998
Chin, V., Gunawan D., Fiebig D. G., Kohn R., & Sisson S.
(2019). Efficient data augmentation for multivariate probit models with panel data: An application to general practitioner decision-making about contraceptives.
arXiv. arXiv:1806.07274v2.
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
Gunawan, D.., Tran M.-N.., Suzuki K.., Dick J.., & Kohn R.
(2019). Computationally efficient Bayesian estimation of high-dimensional Archimedean copulas with discrete and mixed margins.
Statistics and Computing. 29(5), 933-946. doi: 10.1007/s11222-018-9846-y
Dang, K-D., Quiroz M., Kohn R., Tran M-N., & Villani M.
(2019). Hamiltonian Monte Carlo with Energy Conserving Subsampling.
Journal of Machine Learning Research. 20(100), 1-31.
Frazier, D. T., Nott D. J., Drovandi C., & Kohn R.
(2019). Bayesian inference using synthetic likelihood: asymptotics and adjustments.
arXiv. arXiv:1902.04827v2.
Salomone, R., Quiroz M., Kohn R., Villani M., & Tran M-N.
(2019). Spectral Subsampling MCMC for Stationary Time Series.
arXiv. arXiv:1910.13627v1.
Quiroz, M., Villani M., Kohn R., Tran M-N., & Dang K-D.
(2018). Subsampling MCMC - an Introduction for the Survey Statistician.
Sankhya A. 80(1), 33-69. doi: 10.1007/s13171-018-0153-7
Quiroz, M., Nott D. J., & Kohn R.
(2018). Gaussian variational approximations for high-dimensional state space models.
Quiroz, M., Tran M-N., Villani M., & Kohn R.
(2018). Speeding up MCMC by Delayed Acceptance and Data Subsampling.
Journal of Computational and Graphical Statistics. 27(1), 12 - 22. doi: 10.1080/10618600.2017.1307117
Dang, K.. D., Quiroz M., Kohn R., Tran M N., & Villani M.
(2017). Hamiltonian Monte Carlo with Energy Conserving Subsampling.
Gunawan, D., Khaled M., & Kohn R.
(2017). Mixed Marginal Copula Modeling.
Khaled, M., & Kohn R.
(2017). The approximation properties of copulas by mixtures.
Gunawan, D., Carter C., & Kohn R.
(2017). Efficient Bayesian inference for multivariate factor stochastic volatility models with leverage.
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
Khaled, M., & Kohn R.
(2017). The approximation properties of copulas by mixtures.
Gunawan, D., Carter C., Fiebig D. G., & Kohn R.
(2017). Efficient Bayesian Estimation for Flexible Panel Models for Multivariate Outcomes: Impact of Life events on mental health and excessive alcohol consumption.
Del Moral, P., Kohn R., & Patras F.
(2016). On particle Gibbs samplers.
Annales de l'IHP-Probabilités et Statistiques. 52(4), 1687-1733. doi: 10.1214/15-AIHP695
Scharth, M., & Kohn R.
(2016). Particle efficient importance sampling.
Journal of Econometrics. 190(1), 133-147. doi: 10.1016/j.jeconom.2015.03.047
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., 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., 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
Doucet, A., Pitt M. K., Deligiannidis G., & Kohn R.
(2015). Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator.
Biometrika. 102(2), 295-313. doi: 10.1093/biomet/asu075
Del Moral, P., Kohn R., & Patras F.
(2015). A duality formula for Feynman–Kac path particle models.
Comptes Rendus Mathematique. 353(5), 465-469. doi: 10.1016/j.crma.2015.02.008
Peters, G. W., Dong A. X. D., & Kohn R.
(2014). A copula based Bayesian approach for paid–incurred claims models for non-life insurance reserving.
Insurance: Mathematics and Economics. 59, 258-278. doi: 10.1016/j.insmatheco.2014.09.011
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
Balnozan, I., Fiebig D. G., Asher A., Kohn R., & Sisson S.
(Submitted). Hidden Group Time Profiles: Heterogeneous Drawdown Behaviours in Retirement.
Ryan, L. M., Chen V., Beavan A., Speilmann J., Sisson S., & Kohn R.
(2019). A longitudinal analysis of the developmental trajectories of domain specific and domain generic abilities in high-level football players.
Gunawan, D.., Brown S.., Kohn R., & Tran M.. N.
(2018). New Estimation Approaches for the Linear Ballistic Accumulator Model.
Gunawan, D.., Carter C.., & Kohn R.
(2018). Efficiently combining Pseudo Marginal and Particle Gibbs sampling.
Chin, V.., Gunawan D.., Kohn R., & Sisson S.
(2018). Efficient data augmentation for multivariate probit models with panel data: An application to general practitioner decision-making about contraceptives.
Gunawan, D.., Kohn R., Carter C.., & Tran M.. N.
(2018). Flexible density tempering approaches for state space models with an application to factor stochastic volatility models.
Gunawan, D.., Kohn R., Quiroz M.., Dang K.. D., & Tran M.. N.
(2018). Subsampling Sequential Monte Carlo for static Bayesian Models.
Dang, K.-D., Quiroz M., Kohn R., Tran M N., & Villani M.
(2017). Hamiltonian Monte Carlo with Energy Conserving Subsampling.
Gunawan, D., Carter C., & Kohn R.
(2017). Efficient Bayesian inference for multivariate factor stochastic volatility models with leverage.
Gunawan, D., Khaled M., & Kohn R.
(2016). Mixed Marginal Copula Modeling.
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., Kohn R., Quiroz M., & Villani M.
(2016). Block-Wise Pseudo-Marginal Metropolis-Hastings.
Quiroz, M., Villani M., & Kohn R.
(2016). Exact Subsampling MCMC.
Gunawan, D., Tran M N., & Kohn R.
(2016). Fast Inference for Intractable Likelihood Problems using Variational Bayes.