116 citations · 305 across the 40 of their papers we have counts for
13 papers · 1 filter
Inverse Gaussian Process regression for likelihood-free inference
Hongqiao Wang, Ziqiao Ao, Tengchao Yu +1
In this work we consider Bayesian inference problems with intractable likelihood functions. We present a method to compute an approximate of the posterior with a limited number of…
Increasing the efficiency of Sequential Monte Carlo samplers through the use of approximately optimal L-kernels
Peter L Green, Robert E Moore, Ryan J Jackson +2
By facilitating the generation of samples from arbitrary probability distributions, Markov Chain Monte Carlo (MCMC) is, arguably, \emph{the} tool for the evaluation of Bayesian inf…
An approximate KLD based experimental design for models with intractable likelihoods
Ziqiao Ao, Jinglai Li
Data collection is a critical step in statistical inference and data science, and the goal of statistical experimental design (ED) is to find the data collection setup that can pro…
Maximum conditional entropy Hamiltonian Monte Carlo sampler
Tengchao Yu, Hongqiao Wang, Jinglai Li
The performance of Hamiltonian Monte Carlo (HMC) sampler depends critically on some algorithm parameters such as the total integration time and the numerical integration stepsize.…
Bayesian inverse regression for dimension reduction with small datasets
Xin Cai, Guang Lin, Jinglai Li
We consider supervised dimension reduction problems, namely to identify a low dimensional projection of the predictors $\-x$ which can retain the statistical relationship between $…
A weight-bounded importance sampling method for variance reduction
Tengchao Yu, Linjun Lu, Jinglai Li
Importance sampling (IS) is an important technique to reduce the estimation variance in Monte Carlo simulations. In many practical problems, however, the use of IS method may resul…