156 citations · 391 across the 43 of their papers we have counts for
47 papers
Global Neural Networks and The Data Scaling Effect in Financial Time Series Forecasting
Chen Liu, Minh-Ngoc Tran, Chao Wang +2
Neural networks have revolutionized many empirical fields, yet their application to financial time series forecasting remains controversial. In this study, we demonstrate that the…
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…
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…
Deep Learning Enhanced Realized GARCH
Chen Liu, Chao Wang, Minh-Ngoc Tran +1
We propose a new approach to volatility modeling by combining deep learning (LSTM) and realized volatility measures. This LSTM-enhanced realized GARCH framework incorporates and di…
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…