156 citations · 391 across the 43 of their papers we have counts for
6 papers · 1 filter
Bayesian Inference for State Space Models using Block and Correlated Pseudo Marginal Methods
P. Choppala, D. Gunawan, J. Chen +2
This article addresses the problem of efficient Bayesian inference in dynamic systems using particle methods and makes a number of contributions. First, we develop a correlated pse…
Bayesian Adaptive Lasso with Variational Bayes for Variable Selection in High-dimensional Generalized Linear Mixed Models
Dao Thanh Tung, Minh-Ngoc Tran, Tran Manh Cuong
This article describes a full Bayesian treatment for simultaneous fixed-effect selection and parameter estimation in high-dimensional generalized linear mixed models. The approach…
Variational Bayes with Synthetic Likelihood
Victor M-H. Ong, David J. Nott, Minh-Ngoc Tran +2
Synthetic likelihood is an attractive approach to likelihood-free inference when an approximately Gaussian summary statistic for the data, informative for inference about the param…
Computationally Efficient Bayesian Estimation of High Dimensional Copulas with Discrete and Mixed Margins
D. Gunawan, M. -N. Tran, K. Suzuki +2
Estimating copulas with discrete marginal distributions is challenging, especially in high dimensions, because computing the likelihood contribution of each observation requires ev…
The block-Poisson estimator for optimally tuned exact subsampling MCMC
Matias Quiroz, Minh-Ngoc Tran, Mattias Villani +2
Speeding up Markov Chain Monte Carlo (MCMC) for datasets with many observations by data subsampling has recently received considerable attention. A pseudo-marginal MCMC method is p…
The Block Pseudo-Marginal Sampler
M. -N. Tran, R. Kohn, M. Quiroz +1
The pseudo-marginal (PM) approach is increasingly used for Bayesian inference in statistical models, where the likelihood is intractable but can be estimated unbiasedly. %Examples…