14 citations · 18 across the 8 of their papers we have counts for
8 papers
Loss-based Bayesian Sequential Prediction of Value at Risk with a Long-Memory and Non-linear Realized Volatility Model
Rangika Peiris, Minh-Ngoc Tran, Chao Wang +1
A long memory and non-linear realized volatility model class is proposed for direct Value at Risk (VaR) forecasting. This model, referred to as RNN-HAR, extends the heterogeneous a…
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…
Bayesian Inference for Evidence Accumulation Models with Regressors
Viet Hung Dao, David Gunawan, Robert Kohn +3
Evidence accumulation models (EAMs) are an important class of cognitive models used to analyze both response time and response choice data recorded from decision-making tasks. Deve…
An Introduction to Quantum Computing for Statisticians and Data Scientists
Anna Lopatnikova, Minh-Ngoc Tran, Scott A. Sisson
Quantum computers promise to surpass the most powerful classical supercomputers when it comes to solving many critically important practical problems, such as pharmaceutical and fe…
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…