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2023, Particle Mean Field Variational Bayes, , http://dx.doi.org/10.48550/arxiv.2303.13930
,2023, Bayesian Inference for Evidence Accumulation Models with Regressors, , http://arxiv.org/abs/2302.10389v2
,2023, Deep Learning Enhanced Realized GARCH, , http://dx.doi.org/10.48550/arxiv.2302.08002
,2023, Reliable Bayesian Inference in Misspecified Models, , http://dx.doi.org/10.48550/arxiv.2302.06031
,2023, Structured variational approximations with skew normal decomposable graphical models, , http://arxiv.org/abs/2302.03348v1
,2023, The Contextual Lasso: Sparse Linear Models via Deep Neural Networks, , http://arxiv.org/abs/2302.00878v3
,2022, A correlated pseudo-marginal approach to doubly intractable problems, , http://dx.doi.org/10.48550/arxiv.2210.02734
,2022, Automatically adapting the number of state particles in SMC$^2$, , http://dx.doi.org/10.48550/arxiv.2201.11354
,2021, Dynamic Mixture of Experts Models for Online Prediction, , http://dx.doi.org/10.48550/arxiv.2109.11449
,2021, Flexible Variational Bayes based on a Copula of a Mixture, , http://dx.doi.org/10.48550/arxiv.2106.14392
,2021, Modelling age-related changes in executive functions of soccer players, , http://dx.doi.org/10.48550/arxiv.2105.01226
,2021, Spectral Subsampling MCMC for Stationary Multivariate Time Series with Applications to Vector ARTFIMA Processes, , http://dx.doi.org/10.48550/arxiv.2104.02134
,2020, Variational Approximation of Factor Stochastic Volatility Models, , http://dx.doi.org/10.48550/arxiv.2010.06738
,2020, Hidden Group Time Profiles: Heterogeneous Drawdown Behaviours in Retirement, , http://dx.doi.org/10.48550/arxiv.2009.01505
,2019, Spectral Subsampling MCMC for Stationary Time Series, , http://dx.doi.org/10.48550/arxiv.1910.13627
,2019, Identifying relationships between cognitive processes across tasks, contexts, and time, , http://dx.doi.org/10.48550/arxiv.1910.07185
,2019, Multiclass classification of growth curves using random change points and heterogeneous random effects, , http://dx.doi.org/10.48550/arxiv.1909.07550
,2019, Particle Methods for Stochastic Differential Equation Mixed Effects Models, , http://dx.doi.org/10.48550/arxiv.1907.11017
,2019, Time-evolving psychological processes over repeated decisions, , http://dx.doi.org/10.48550/arxiv.1906.10838
,2019, Robustly estimating the marginal likelihood for cognitive models via importance sampling, , http://dx.doi.org/10.48550/arxiv.1906.06020
,2019, A Statistical Recurrent Stochastic Volatility Model for Stock Markets, , http://dx.doi.org/10.48550/arxiv.1906.02884
,2019, Bayesian inference using synthetic likelihood: asymptotics and adjustments, , http://dx.doi.org/10.48550/arxiv.1902.04827
,2018, Variance reduction properties of the reparameterization trick, , http://dx.doi.org/10.48550/arxiv.1809.10330
,2018, Subsampling MCMC - An introduction for the survey statistician, , http://dx.doi.org/10.48550/arxiv.1807.08409
,2018, New Estimation Approaches for the Hierarchical Linear Ballistic Accumulator Model, , http://dx.doi.org/10.48550/arxiv.1806.10089
,2018, Efficient data augmentation for multivariate probit models with panel data: An application to general practitioner decision-making about contraceptives, , http://dx.doi.org/10.48550/arxiv.1806.07274
,2018, Bayesian Deep Net GLM and GLMM, , http://dx.doi.org/10.48550/arxiv.1805.10157
,2018, Subsampling Sequential Monte Carlo for Static Bayesian Models, , http://dx.doi.org/10.48550/arxiv.1805.03317
,2018, Robust Particle Density Tempering for State Space Models, , http://dx.doi.org/10.48550/arxiv.1805.00649
,2017, Hamiltonian Monte Carlo with Energy Conserving Subsampling, , http://dx.doi.org/10.48550/arxiv.1708.00955
,2017, Fast Inference for Intractable Likelihood Problems using Variational Bayes, , http://dx.doi.org/10.48550/arxiv.1705.06679
,2016, The block-Poisson estimator for optimally tuned exact subsampling MCMC, , http://dx.doi.org/10.48550/arxiv.1603.08232
,2015, Speeding Up MCMC by Delayed Acceptance and Data Subsampling, , http://dx.doi.org/10.48550/arxiv.1507.06110
,2015, Scalable MCMC for Large Data Problems using Data Subsampling and the Difference Estimator, , http://dx.doi.org/10.48550/arxiv.1507.02971
,2015, Variational Bayes with Intractable Likelihood, , http://dx.doi.org/10.48550/arxiv.1503.08621
,2015, Markov Interacting Importance Samplers, , http://dx.doi.org/10.48550/arxiv.1502.07039
,2014, Speeding Up MCMC by Efficient Data Subsampling, , http://dx.doi.org/10.48550/arxiv.1404.4178
,2014, A flexible Particle Markov chain Monte Carlo method, , http://dx.doi.org/10.48550/arxiv.1401.1667
,2013, Particle Efficient Importance Sampling, , http://dx.doi.org/10.48550/arxiv.1309.6745
,2013, Importance sampling squared for Bayesian inference in latent variable models, , http://dx.doi.org/10.48550/arxiv.1309.3339
,2013, Efficient variational inference for generalized linear mixed models with large datasets, , http://dx.doi.org/10.48550/arxiv.1307.7963
,2013, On the existence of moments for high dimensional importance sampling, , http://dx.doi.org/10.48550/arxiv.1307.7975
,2013, Copula-type Estimators for Flexible Multivariate Density Modeling using Mixtures, , http://dx.doi.org/10.48550/arxiv.1306.3033
,2013, Adaptive Metropolis-Hastings Sampling using Reversible Dependent Mixture Proposals, , http://dx.doi.org/10.48550/arxiv.1305.2634
,2012, A Copula Based Bayesian Approach for Paid-Incurred Claims Models for Non-Life Insurance Reserving, , http://dx.doi.org/10.48550/arxiv.1210.3849
,2012, Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator, , http://dx.doi.org/10.48550/arxiv.1210.1871
,2010, A copula based approach to adaptive sampling, , http://dx.doi.org/10.48550/arxiv.1002.4775
,2009, Flexible Multivariate Density Estimation with Marginal Adaptation, , http://dx.doi.org/10.48550/arxiv.0901.0225
,2007, Locally Adaptive Nonparametric Binary Regression, , http://dx.doi.org/10.48550/arxiv.0709.3545
,2007, Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models, , http://dx.doi.org/10.48550/arxiv.0707.2158
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