15 citations · 55 across the 9 of their papers we have counts for
9 papers
Prior normalization for certified likelihood-informed subspace detection of Bayesian inverse problems
Tiangang Cui, Xin Tong, Olivier Zahm
Markov chain Monte Carlo (MCMC) methods form one of the algorithmic foundations of Bayesian inverse problems. The recent development of likelihood-informed subspace (LIS) methods o…
A unified performance analysis of likelihood-informed subspace methods
Tiangang Cui, Xin T. Tong
The likelihood-informed subspace (LIS) method offers a viable route to reducing the dimensionality of high-dimensional probability distributions arising in Bayesian inference. LIS…
MALA-within-Gibbs samplers for high-dimensional distributions with sparse conditional structure
X. T. Tong, M. Morzfeld, Y. M. Marzouk
Markov chain Monte Carlo (MCMC) samplers are numerical methods for drawing samples from a given target probability distribution. We discuss one particular MCMC sampler, the MALA-wi…
Localization for MCMC: sampling high-dimensional posterior distributions with local structure
Matthias Morzfeld, Xin T. Tong, Youssef M. Marzouk
We investigate how ideas from covariance localization in numerical weather prediction can be used in Markov chain Monte Carlo (MCMC) sampling of high-dimensional posterior distribu…
Feature Augmentation via Nonparametrics and Selection (FANS) in High Dimensional Classification
Jianqing Fan, Yang Feng, Jiancheng Jiang +1
We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifi…
Communication Learning in Social Networks: Finite Population and the Rates
Jianqing Fan, Xin Tong, Yao Zeng
Following the Bayesian communication learning paradigm, we propose a finite population learning concept to capture the level of information aggregation in any given network, where…