By Scientia Professor Robert Kohn

Preprints

Yang Y; Quiroz M; Beranger B; Kohn R; Sisson SA, 2024, Analysing symbolic data by pseudo-marginal methods, http://arxiv.org/abs/2408.04419v1

Tseng Y-H; Tran M-N; Kohn R, 2024, Variational Bayesian Inference for Modelswith Nuisance Parameters and an Intractable Likelihood, http://dx.doi.org/10.21203/rs.3.rs-4487816/v1

Tran M-N; Tseng P; Kohn R, 2023, Particle Mean Field Variational Bayes, http://arxiv.org/abs/2303.13930v2

Dao VH; Gunawan D; Kohn R; Tran M-N; Hawkins GE; Brown SD, 2023, Bayesian Inference for Evidence Accumulation Models with Regressors, http://arxiv.org/abs/2302.10389v2

Liu C; Wang C; Tran M-N; Kohn R, 2023, Deep Learning Enhanced Realized GARCH, http://arxiv.org/abs/2302.08002v2

Frazier DT; Kohn R; Drovandi C; Gunawan D, 2023, Reliable Bayesian Inference in Misspecified Models, http://arxiv.org/abs/2302.06031v1

Salomone R; Yu X; Nott DJ; Kohn R, 2023, Structured variational approximations with skew normal decomposable graphical models, http://arxiv.org/abs/2302.03348v1

Thompson R; Dezfouli A; Kohn R, 2023, The Contextual Lasso: Sparse Linear Models via Deep Neural Networks, http://arxiv.org/abs/2302.00878v4

Yang Y; Quiroz M; Kohn R; Sisson SA, 2022, A correlated pseudo-marginal approach to doubly intractable problems, http://arxiv.org/abs/2210.02734v1

Botha I; Kohn R; South L; Drovandi C, 2022, Automatically adapting the number of state particles in SMC$^2$, http://arxiv.org/abs/2201.11354v2

Gunawan D; Chatterjee P; Kohn R, 2021, The Block-Correlated Pseudo Marginal Sampler for State Space Models, http://arxiv.org/abs/2109.14194v2

Munezero P; Villani M; Kohn R, 2021, Dynamic Mixture of Experts Models for Online Prediction, http://arxiv.org/abs/2109.11449v2

Gunawan D; Kohn R; Nott D, 2021, Flexible Variational Bayes based on a Copula of a Mixture, http://arxiv.org/abs/2106.14392v4

Chin V; Beavan A; Fransen J; Mayer J; Kohn R; Ryan LM; Sisson SA, 2021, Modelling age-related changes in executive functions of soccer players, http://arxiv.org/abs/2105.01226v1

Villani M; Quiroz M; Kohn R; Salomone R, 2021, Spectral Subsampling MCMC for Stationary Multivariate Time Series with Applications to Vector ARTFIMA Processes, http://arxiv.org/abs/2104.02134v2

Dao V-H; Gunawan D; Tran M-N; Kohn R; Hawkins GE; Brown SD, 2021, Efficient Selection Between Hierarchical Cognitive Models: Cross-validation With Variational Bayes, http://arxiv.org/abs/2102.06814v2

Gunawan D; Kohn R; Nott D, 2020, Variational Approximation of Factor Stochastic Volatility Models, http://arxiv.org/abs/2010.06738v2

Balnozan I; Fiebig DG; Asher A; Kohn R; Sisson SA, 2020, Hidden Group Time Profiles: Heterogeneous Drawdown Behaviours in Retirement, http://arxiv.org/abs/2009.01505v3

Salomone R; Quiroz M; Kohn R; Villani M; Tran M-N, 2019, Spectral Subsampling MCMC for Stationary Time Series, http://arxiv.org/abs/1910.13627v2

Wall L; Gunawan D; Brown SD; Tran M-N; Kohn R; Hawkins GE, 2019, Identifying relationships between cognitive processes across tasks, contexts, and time, http://arxiv.org/abs/1910.07185v2

Chin V; Lee JYL; Ryan LM; Kohn R; Sisson SA, 2019, Multiclass classification of growth curves using random change points and heterogeneous random effects, http://arxiv.org/abs/1909.07550v1

Botha I; Kohn R; Drovandi C, 2019, Particle Methods for Stochastic Differential Equation Mixed Effects Models, http://arxiv.org/abs/1907.11017v2

Gunawan D; Hawkins GE; Kohn R; Tran M-N; Brown SD, 2019, Time-evolving psychological processes over repeated decisions, http://arxiv.org/abs/1906.10838v3

Tran M-N; Scharth M; Gunawan D; Kohn R; Brown SD; Hawkins GE, 2019, Robustly estimating the marginal likelihood for cognitive models via importance sampling, http://arxiv.org/abs/1906.06020v2

Nguyen T-N; Tran M-N; Gunawan D; Kohn R, 2019, A Statistical Recurrent Stochastic Volatility Model for Stock Markets, http://arxiv.org/abs/1906.02884v3

Frazier DT; Nott DJ; Drovandi C; Kohn R, 2019, Bayesian inference using synthetic likelihood: asymptotics and adjustments, http://arxiv.org/abs/1902.04827v4

Xu M; Quiroz M; Kohn R; Sisson SA, 2018, Variance reduction properties of the reparameterization trick, http://arxiv.org/abs/1809.10330v3

Quiroz M; Villani M; Kohn R; Tran M-N; Dang K-D, 2018, Subsampling MCMC - An introduction for the survey statistician, http://arxiv.org/abs/1807.08409v4

Gunawan D; Hawkins GE; Tran M-N; Kohn R; Brown S, 2018, New Estimation Approaches for the Hierarchical Linear Ballistic Accumulator Model, http://arxiv.org/abs/1806.10089v5

Chin V; Gunawan D; Fiebig DG; Kohn R; Sisson SA, 2018, Efficient data augmentation for multivariate probit models with panel data: An application to general practitioner decision-making about contraceptives, http://dx.doi.org/10.1111/rssc.12393

Tran M-N; Nguyen N; Nott D; Kohn R, 2018, Bayesian Deep Net GLM and GLMM, http://arxiv.org/abs/1805.10157v1

Gunawan D; Dang K-D; Quiroz M; Kohn R; Tran M-N, 2018, Subsampling Sequential Monte Carlo for Static Bayesian Models, http://arxiv.org/abs/1805.03317v3

Gunawan D; Kohn R; Tran MN, 2018, Robust Particle Density Tempering for State Space Models, http://dx.doi.org/10.48550/arxiv.1805.00649

Quiroz M; Nott DJ; Kohn R, 2018, Gaussian variational approximation for high-dimensional state space models, http://arxiv.org/abs/1801.07873v3

Dang K-D; Quiroz M; Kohn R; Tran M-N; Villani M, 2017, Hamiltonian Monte Carlo with Energy Conserving Subsampling, http://arxiv.org/abs/1708.00955v3

Khaled MA; Kohn R, 2017, On approximating copulas by finite mixtures, http://arxiv.org/abs/1705.10440v3

Gunawan D; Tran M-N; Kohn R, 2017, Fast Inference for Intractable Likelihood Problems using Variational Bayes, http://arxiv.org/abs/1705.06679v1

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, http://arxiv.org/abs/1608.06174v3

Gunawan D; Khaled MA; Kohn R, 2016, Mixed Marginal Copula Modeling, http://arxiv.org/abs/1605.09101v3

Quiroz M; Tran M-N; Villani M; Kohn R; Dang K-D, 2016, The block-Poisson estimator for optimally tuned exact subsampling MCMC, http://arxiv.org/abs/1603.08232v6

Tran M-N; Kohn R; Quiroz M; Villani M, 2016, The Block Pseudo-Marginal Sampler, http://arxiv.org/abs/1603.02485v5

Tran MN; Kohn R, 2015, Exact ABC using Importance Sampling, http://arxiv.org/abs/1509.08076v1

Quiroz M; Tran M-N; Villani M; Kohn R, 2015, Speeding Up MCMC by Delayed Acceptance and Data Subsampling, http://dx.doi.org/10.1080/10618600.2017.1307117

Quiroz M; Villani M; Kohn R, 2015, Scalable MCMC for Large Data Problems using Data Subsampling and the Difference Estimator, http://arxiv.org/abs/1507.02971v3

Pitt MK; Hall J; Kohn R, 2015, Bayesian inference for latent factor GARCH models, http://arxiv.org/abs/1507.01179v1

Tran M-N; Nott DJ; Kohn R, 2015, Variational Bayes with Intractable Likelihood, http://arxiv.org/abs/1503.08621v2

Mendes EF; Scharth M; Kohn R, 2015, Markov Interacting Importance Samplers, http://arxiv.org/abs/1502.07039v2

Quiroz M; Kohn R; Villani M; Tran M-N, 2014, Speeding Up MCMC by Efficient Data Subsampling, http://dx.doi.org/10.1080/01621459.2018.1448827

Mendes EF; Carter CK; Gunawan D; Kohn R, 2014, A flexible Particle Markov chain Monte Carlo method, http://dx.doi.org/10.48550/arxiv.1401.1667

Scharth M; Kohn R, 2013, Particle Efficient Importance Sampling, http://arxiv.org/abs/1309.6745v1

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