48 citations · 153 across the 26 of their papers we have counts for
5 papers · 1 filter
Stochastic Approximation Hamiltonian Monte Carlo
Jonghyun Yun, Minsuk Shin, Ick Hoon Jin +1
Recently, the Hamilton Monte Carlo (HMC) has become widespread as one of the more reliable approaches to efficient sample generation processes. However, HMC is difficult to sample…
Learning Gene Regulatory Networks with High-Dimensional Heterogeneous Data
Bochao Jia, Faming Liang
The Gaussian graphical model is a widely used tool for learning gene regulatory networks with high-dimensional gene expression data. Most existing methods for Gaussian graphical mo…
Fast Bayesian Integrative Learning of Multiple Gene Regulatory Networks for Type 1 Diabetes
Bochao Jia, Faming Liang, the TEDDY Study Group
Motivated by the need to study the molecular mechanism underlying Type 1 Diabetes (T1D) with the gene expression data collected from both the patients and healthy controls at multi…
A Bayesian Mark Interaction Model for Analysis of Tumor Pathology Images
Qiwei Li, Xinlei Wang, Faming Liang +1
With the advance of imaging technology, digital pathology imaging of tumor tissue slides is becoming a routine clinical procedure for cancer diagnosis. This process produces massiv…
An Imputation-Consistency Algorithm for High-Dimensional Missing Data Problems and Beyond
Faming Liang, Bochao Jia, Jingnan Xue +2
Missing data are frequently encountered in high-dimensional problems, but they are usually difficult to deal with using standard algorithms, such as the expectation-maximization (E…