156 citations · 391 across the 43 of their papers we have counts for
10 papers · 1 filter
Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic Likelihood
Nhat-Minh Nguyen, Minh-Ngoc Tran, Christopher Drovandi +1
The Bayesian Synthetic Likelihood (BSL) method is a widely-used tool for likelihood-free Bayesian inference. This method assumes that some summary statistics are normally distribut…
Particle Mean Field Variational Bayes
Minh-Ngoc Tran, Paco Tseng, Robert Kohn
The Mean Field Variational Bayes (MFVB) method is one of the most computationally efficient techniques for Bayesian inference. However, its use has been restricted to models with c…
A practical tutorial on Variational Bayes
Minh-Ngoc Tran, Trong-Nghia Nguyen, Viet-Hung Dao
This tutorial gives a quick introduction to Variational Bayes (VB), also called Variational Inference or Variational Approximation, from a practical point of view. The paper covers…
Assessment and adjustment of approximate inference algorithms using the law of total variance
Xuejun Yu, David J. Nott, Minh-Ngoc Tran +1
A common method for assessing validity of Bayesian sampling or approximate inference methods makes use of simulated data replicates for parameters drawn from the prior. Under conti…
Robustly estimating the marginal likelihood for cognitive models via importance sampling
Minh-Ngoc Tran, Marcel Scharth, David Gunawan +3
Recent advances in Markov chain Monte Carlo (MCMC) extend the scope of Bayesian inference to models for which the likelihood function is intractable. Although these developments al…
Bayesian Deep Net GLM and GLMM
Minh-Ngoc Tran, Nghia Nguyen, David Nott +1
Deep feedforward neural networks (DFNNs) are a powerful tool for functional approximation. We describe flexible versions of generalized linear and generalized linear mixed models i…