- Home
- People
- Chief Investigators
- 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
Publications
Invited talks, refereed proceedings and other conference outputs
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.
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.
Gunawan, D.., Carter C.., & Kohn R.
(2018). Efficiently combining Pseudo Marginal and Particle Gibbs sampling.
Joint Statistical Meeting 2018.
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
Botha, I., Kohn R., & Drovandi C.
(2021). Particle Methods for Stochastic Differential Equation Mixed Effects Models.
Bayesian Analysis. 16(2), 575-609. doi: 10.1214/20-BA1216
Mendes, E.. F., Carter C.. K., Gunawan D.., & Kohn R.
(2020). A Flexible particle Markov chain Monte Carlo Method.
Statistics and Computing. 30, 783-798. doi: 10.1007/s11222-019-09916-7
Chin, V.., Gunawan D.., Fiebig D.., Kohn R., & Sisson S.
(2020). Efficient data augmentation for multivariate probit models with panel data: An application to general practitioner decision-making about contraceptives.
Journal of Royal Statistical Society. 69(2), 277-300. doi: 10.1111/rssc.12393
Balnozan, I., Fiebig D. G., Asher A., Kohn R., & Sisson S.
(2020). Hidden Group Time Profiles: Heterogeneous Drawdown Behaviours in Retirement.
arXiv. arXiv:2009.01505v1,
Tran, M-N., Nguyen N., Nott D., & Kohn R.
(2020). Bayesian Deep Net GLM and GLMM.
Journal of Computational and Graphical Statistics. 29, 97-113. doi: 10.1080/10618600.2019.1637747
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., 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
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.
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.
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
Quiroz, M., Nott D. J., & Kohn R.
(2018). Gaussian variational approximations for high-dimensional state space models.
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
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.
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.
Dang, K.. D., Quiroz M., Kohn R., Tran M N., & Villani M.
(2017). Hamiltonian Monte Carlo with Energy Conserving Subsampling.
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
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
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
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.., Kohn R., Quiroz M.., Dang K.. D., & Tran M.. N.
(2018). Subsampling Sequential Monte Carlo for static Bayesian Models.
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.
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.