320 citations · 349 across the 10 of their papers we have counts for
10 papers
Non-convex sampling for a mixture of locally smooth potentials
Dao Nguyen
The purpose of this paper is to examine the sampling problem through Euler discretization, where the potential function is assumed to be a mixture of locally smooth distributions a…
Unadjusted Langevin algorithm for sampling a mixture of weakly smooth potentials
Dao Nguyen
Discretization of continuous-time diffusion processes is a widely recognized method for sampling. However, it seems to be a considerable restriction when the potentials are often r…
Unadjusted Langevin algorithm for non-convex weakly smooth potentials
Dao Nguyen, Xin Dang, Yixin Chen
Discretization of continuous-time diffusion processes is a widely recognized method for sampling. However, the canonical Euler Maruyama discretization of the Langevin diffusion pro…
Black-box sampling for weakly smooth Langevin Monte Carlo using p-generalized Gaussian smoothing
Anh Duc Doan, Xin Dang, Dao Nguyen
Discretization of continuous-time diffusion processes is a widely recognized method for sampling. However, the canonical Euler-Maruyama discretization of the Langevin diffusion pro…
Estimating Feature-Label Dependence Using Gini Distance Statistics
Silu Zhang, Xin Dang, Dao Nguyen +2
Identifying statistical dependence between the features and the label is a fundamental problem in supervised learning. This paper presents a framework for estimating dependence bet…
Simulation-based inference methods for partially observed Markov model via the R package is2
Duc Anh Doan, Dao Nguyen, Xin Dang
Partially observed Markov process (POMP) models are powerful tools for time series modeling and analysis. Inherited the flexible framework of R package pomp, the is2 package extend…
A new Gini correlation between quantitative and qualitative variables
Xin Dang, Dao Nguyen, Yixin Chen +1
We propose a new Gini correlation to measure dependence between a categorical and numerical variables. Analogous to Pearson in ANOVA model, the Gini correlation is interprete…
Accelerate iterated filtering
Dao Nguyen
In simulation-based inferences for partially observed Markov process models (POMP), the by-product of the Monte Carlo filtering is an approximation of the log likelihood function.…
Automatic adaptation of MCMC algorithms
Dao Nguyen, Perry de Valpine, Yves Atchade +3
Markov chain Monte Carlo (MCMC) methods are ubiquitous tools for simulation-based inference in many fields but designing and identifying good MCMC samplers is still an open questio…
Statistical Inference for Partially Observed Markov Processes via the R Package pomp
Aaron A. King, Dao Nguyen, Edward L. Ionides
Partially observed Markov process (POMP) models, also known as hidden Markov models or state space models, are ubiquitous tools for time series analysis. The R package pomp provide…