17 citations · 37 across the 9 of their papers we have counts for
10 papers
Variational approximate penalized credible regions for Bayesian grouped regression
Weichang Yu, Khue-Dung Dang
We develop a fast and accurate grouped penalized credible region approach for variable selection and prediction in Bayesian high-dimensional regression. Most existing Bayesian meth…
Variational Bayes for Mixture of Gaussian Structural Equation Models
Khue-Dung Dang, Luca Maestrini, Francis K. C. Hui
Structural equation models (SEMs) are commonly used to study the structural relationship between observed variables and latent constructs. Recently, Bayesian fitting procedures for…
Mixtures of Gaussian process experts based on kernel stick-breaking processes
Yuji Saikai, Khue-Dung Dang
Mixtures of Gaussian process experts is a class of models that can simultaneously address two of the key limitations inherent in standard Gaussian processes: scalability and predic…
Bayesian outcome selection modelling
Khue-Dung Dang, Louise M. Ryan, Richard J. Cook +3
Psychiatric and social epidemiology often involves assessing the effects of environmental exposure on outcomes that are difficult to measure directly. To address this problem, it i…
Fitting Structural Equation Models via Variational Approximations
Khue-Dung Dang, Luca Maestrini
Structural equation models are commonly used to capture the relationship between sets of observed and unobservable variables. Traditionally these models are fitted using frequentis…
Bayesian structural equation modeling for data from multiple cohorts
Khue-Dung Dang, Louise M. Ryan, Tugba Akkaya-Hocagil +7
While it is well known that high levels of prenatal alcohol exposure (PAE) result in significant cognitive deficits in children, the exact nature of the dose response is less well…
Subsampling MCMC - An introduction for the survey statistician
Matias Quiroz, Mattias Villani, Robert Kohn +2
The rapid development of computing power and efficient Markov Chain Monte Carlo (MCMC) simulation algorithms have revolutionized Bayesian statistics, making it a highly practical i…
Subsampling Sequential Monte Carlo for Static Bayesian Models
David Gunawan, Khue-Dung Dang, Matias Quiroz +2
We show how to speed up Sequential Monte Carlo (SMC) for Bayesian inference in large data problems by data subsampling. SMC sequentially updates a cloud of particles through a sequ…
Hamiltonian Monte Carlo with Energy Conserving Subsampling
Khue-Dung Dang, Matias Quiroz, Robert Kohn +2
Hamiltonian Monte Carlo (HMC) samples efficiently from high-dimensional posterior distributions with proposed parameter draws obtained by iterating on a discretized version of the…
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…