6 papers
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…
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…