By Scientia Professor Robert Kohn

Preprints

Fagerberg G; Villani M; Kohn R, 2026, Time-Varying Multi-Seasonal ARMA Models

Yang Y; Quiroz M; Beranger B; Kohn R; Sisson SA, 2026, Analysing symbolic data by pseudo-marginal methods, http://dx.doi.org/10.48550/arxiv.2408.04419

Frazier DT; Drovandi C; Kohn R, 2026, Calibrated Generalized Bayesian Inference, http://dx.doi.org/10.48550/arxiv.2311.15485

Fagerberg G; Villani M; Kohn R, 2025, Time-Varying Multi-Seasonal AR Models, http://dx.doi.org/10.48550/arxiv.2409.18640

Yang Y; Quiroz M; Kohn R; Sisson SA, 2025, A correlated pseudo-marginal approach to doubly intractable problems, http://dx.doi.org/10.48550/arxiv.2210.02734

Balnozan I; Fiebig DG; Asher A; Kohn R; Sisson SA, 2025, Hidden Group Time Profiles: Heterogeneous Drawdown Behaviours in Retirement, http://dx.doi.org/10.48550/arxiv.2009.01505

Liu C; Tran M-N; Wang C; Gerlach R; Kohn R, 2025, Global Neural Networks and The Data Scaling Effect in Financial Time Series Forecasting, http://dx.doi.org/10.48550/arxiv.2309.02072

Rodrigo LM; Kohn R; Afshar HM; Cripps S, 2025, A Beta Cauchy-Cauchy (BECCA) shrinkage prior for Bayesian variable selection, http://dx.doi.org/10.48550/arxiv.2501.07061

Liu C; Wang C; Tran M-N; Kohn R, 2023, Deep Learning Enhanced Realized GARCH, http://dx.doi.org/10.48550/arxiv.2302.08002

Gunawan D; Kohn R; Nott D, 2023, Flexible Variational Bayes based on a Copula of a Mixture, http://dx.doi.org/10.48550/arxiv.2106.14392

Botha I; Kohn R; South L; Drovandi C, 2023, Adaptively switching between a particle marginal Metropolis-Hastings and a particle Gibbs kernel in SMC$^2$, http://dx.doi.org/10.48550/arxiv.2307.11553

Dao VH; Gunawan D; Kohn R; Tran M-N; Hawkins GE; Brown SD, 2023, Bayesian Inference for Evidence Accumulation Models with Regressors, http://dx.doi.org/10.48550/arxiv.2302.10389

Tran M-N; Tseng P; Kohn R, 2023, Particle Mean Field Variational Bayes, http://dx.doi.org/10.48550/arxiv.2303.13930

Gunawan D; Chatterjee P; Kohn R, 2023, The Block-Correlated Pseudo Marginal Sampler for State Space Models, http://dx.doi.org/10.48550/arxiv.2109.14194

Frazier DT; Kohn R; Drovandi C; Gunawan D, 2023, Reliable Bayesian Inference in Misspecified Models, http://dx.doi.org/10.48550/arxiv.2302.06031

Salomone R; Yu X; Nott DJ; Kohn R, 2023, Structured variational approximations with skew normal decomposable graphical models, http://dx.doi.org/10.48550/arxiv.2302.03348

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

Botha I; Kohn R; South L; Drovandi C, 2022, Automatically adapting the number of state particles in SMC$^2$, http://dx.doi.org/10.48550/arxiv.2201.11354

Munezero P; Villani M; Kohn R, 2022, Dynamic Mixture of Experts Models for Online Prediction, http://dx.doi.org/10.48550/arxiv.2109.11449

Villani M; Quiroz M; Kohn R; Salomone R, 2022, Spectral Subsampling MCMC for Stationary Multivariate Time Series with Applications to Vector ARTFIMA Processes, http://dx.doi.org/10.48550/arxiv.2104.02134

Gunawan D; Kohn R; Tran MN, 2022, Robust Particle Density Tempering for State Space Models, http://dx.doi.org/10.48550/arxiv.1805.00649

Nguyen T-N; Tran M-N; Gunawan D; Kohn R, 2022, A Statistical Recurrent Stochastic Volatility Model for Stock Markets, http://dx.doi.org/10.48550/arxiv.1906.02884

Gunawan D; Hawkins GE; Kohn R; Tran M-N; Brown SD, 2021, Time-evolving psychological processes over repeated decisions, http://dx.doi.org/10.48550/arxiv.1906.10838

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://dx.doi.org/10.48550/arxiv.2102.06814

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://dx.doi.org/10.48550/arxiv.2105.01226

Gunawan D; Kohn R; Nott D, 2021, Variational Approximation of Factor Stochastic Volatility Models, http://dx.doi.org/10.48550/arxiv.2010.06738

Frazier DT; Nott DJ; Drovandi C; Kohn R, 2021, Bayesian inference using synthetic likelihood: asymptotics and adjustments, http://dx.doi.org/10.48550/arxiv.1902.04827

Quiroz M; Tran M-N; Villani M; Kohn R; Dang K-D, 2020, The block-Poisson estimator for optimally tuned exact subsampling MCMC, http://dx.doi.org/10.48550/arxiv.1603.08232

Wall L; Gunawan D; Brown SD; Tran M-N; Kohn R; Hawkins GE, 2020, Identifying relationships between cognitive processes across tasks, contexts, and time, http://dx.doi.org/10.48550/arxiv.1910.07185

Gunawan D; Dang K-D; Quiroz M; Kohn R; Tran M-N, 2020, Subsampling Sequential Monte Carlo for Static Bayesian Models, http://dx.doi.org/10.48550/arxiv.1805.03317

Gunawan D; Hawkins GE; Tran M-N; Kohn R; Brown S, 2020, New Estimation Approaches for the Hierarchical Linear Ballistic Accumulator Model, http://dx.doi.org/10.48550/arxiv.1806.10089

Quiroz M; Nott DJ; Kohn R, 2020, Gaussian variational approximation for high-dimensional state space models, http://dx.doi.org/10.48550/arxiv.1801.07873

Salomone R; Quiroz M; Kohn R; Villani M; Tran M-N, 2020, Spectral Subsampling MCMC for Stationary Time Series, http://dx.doi.org/10.48550/arxiv.1910.13627

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://dx.doi.org/10.48550/arxiv.1906.06020

Mendes EF; Carter CK; Gunawan D; Kohn R, 2019, A flexible Particle Markov chain Monte Carlo method, http://dx.doi.org/10.48550/arxiv.1401.1667

Botha I; Kohn R; Drovandi C, 2019, Particle Methods for Stochastic Differential Equation Mixed Effects Models, http://dx.doi.org/10.48550/arxiv.1907.11017

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://dx.doi.org/10.48550/arxiv.1909.07550

Chin V; Gunawan D; Fiebig DG; Kohn R; Sisson SA, 2019, Efficient data augmentation for multivariate probit models with panel data: An application to general practitioner decision-making about contraceptives, http://dx.doi.org/10.48550/arxiv.1806.07274

Dang K-D; Quiroz M; Kohn R; Tran M-N; Villani M, 2019, Hamiltonian Monte Carlo with Energy Conserving Subsampling, http://dx.doi.org/10.48550/arxiv.1708.00955

Xu M; Quiroz M; Kohn R; Sisson SA, 2018, Variance reduction properties of the reparameterization trick, http://dx.doi.org/10.48550/arxiv.1809.10330

Quiroz M; Villani M; Kohn R; Tran M-N; Dang K-D, 2018, Subsampling MCMC - An introduction for the survey statistician, http://dx.doi.org/10.48550/arxiv.1807.08409

Tran M-N; Nguyen N; Nott D; Kohn R, 2018, Bayesian Deep Net GLM and GLMM, http://dx.doi.org/10.48550/arxiv.1805.10157

Quiroz M; Kohn R; Villani M; Tran M-N, 2018, Speeding Up MCMC by Efficient Data Subsampling, http://dx.doi.org/10.48550/arxiv.1404.4178

Gunawan D; Khaled MA; Kohn R, 2017, Mixed Marginal Copula Modeling, http://dx.doi.org/10.48550/arxiv.1605.09101

Quiroz M; Villani M; Kohn R, 2017, Scalable MCMC for Large Data Problems using Data Subsampling and the Difference Estimator, http://dx.doi.org/10.48550/arxiv.1507.02971

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

Gunawan D; Tran M-N; Kohn R, 2017, Fast Inference for Intractable Likelihood Problems using Variational Bayes, http://dx.doi.org/10.48550/arxiv.1705.06679

Quiroz M; Tran M-N; Villani M; Kohn R, 2017, Speeding Up MCMC by Delayed Acceptance and Data Subsampling, http://dx.doi.org/10.48550/arxiv.1507.06110

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

Tran M-N; Nott DJ; Kohn R, 2016, Variational Bayes with Intractable Likelihood, http://dx.doi.org/10.48550/arxiv.1503.08621

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