8 citations · 16 across the 10 of their papers we have counts for
10 papers
Bayesian factorization via shrinkage
Shicheng Liu, Qingping Zhou, Yanan Fan +1
Factor models are widely used for dimension reduction. Bayesian approaches to these models often place a prior on the factor loadings that allows for infinitely many factors, with…
Fused prior for large scale linear inverse problem with Gibbs bouncy particle sampler
Xiongwen Ke, Yanan Fan, Qingping Zhou
In this paper, we study Bayesian approach for solving large scale linear inverse problems arising in various scientific and engineering fields. We propose a fused prior w…
Deep Unrolling Networks with Recurrent Momentum Acceleration for Nonlinear Inverse Problems
Qingping Zhou, Jiayu Qian, Junqi Tang +1
Combining the strengths of model-based iterative algorithms and data-driven deep learning solutions, deep unrolling networks (DuNets) have become a popular tool to solve inverse im…
MCDIP-ADMM: Overcoming Overfitting in DIP-based CT reconstruction
Chen Cheng, Qingping Zhou
This paper investigates the application of unsupervised learning methods for computed tomography (CT) reconstruction. To motivate our work, we review several existing priors, namel…
Enhancing Electrical Impedance Tomography reconstruction using Learned Half-Quadratic Splitting Networks with Anderson Acceleration
Guixian Xu, Huihui Wang, Qingping Zhou
Electrical Impedance Tomography (EIT) is widely applied in medical diagnosis, industrial inspection, and environmental monitoring. Combining the physical principles of the imaging…
Inferring the unknown parameters in Differential Equation by Gaussian Process Regression with Constraint
Ying Zhou, Hongqiao Wang
Differential Equation (DE) is a commonly used modeling method in various scientific subjects such as finance and biology. The parameters in DE models often have interesting scienti…
Bayesian inference and uncertainty quantification for image reconstruction with Poisson data
Qingping Zhou, Tengchao Yu, Xiaoqun Zhang +1
We provide a complete framework for performing infinite-dimensional Bayesian inference and uncertainty quantification for image reconstruction with Poisson data. In particular, we…
NLTG Priors in Medical Image: Nonlocal TV-Gaussian (NLTG) prior for Bayesian inverse problems with applications to Limited CT Reconstruction
Didi Lv, Qingping Zhou, Jae Kyu Choi +2
Bayesian inference methods have been widely applied in inverse problems, {largely due to their ability to characterize the uncertainty associated with the estimation results.} {In…
An approximate empirical Bayesian method for large-scale linear-Gaussian inverse problems
Qingping Zhou, Wenqing Liu, Jinglai Li +1
We study Bayesian inference methods for solving linear inverse problems, focusing on hierarchical formulations where the prior or the likelihood function depend on unspecified hype…
A hybrid adaptive MCMC algorithm in function spaces
Qingping Zhou, Zixi Hu, Zhewei Yao +1
The preconditioned Crank-Nicolson (pCN) method is a Markov Chain Monte Carlo (MCMC) scheme, specifically designed to perform Bayesian inferences in function spaces. Unlike many sta…