Heterogeneity

Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data

Graphical Dirichlet process.

Joint Bayesian Estimation of Cell Dependence and Gene Associations in Spatially Resolved Transcriptomic Data

Recent technologies such as spatial transcriptomics, enable the measurement of gene expressions at the single-cell level along with the spatial locations of these cells in the tissue. Spatial clustering of the cells provides valuable insights into …

Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data

A new Gaussian graphical model that produces subject-specific and predictive graphs with theoretical guarantee.

Individualized Causal Discovery with Latent Trajectory Embedded Bayesian Networks

Individualized causal discovery.

Individualized Inference in Bayesian Quantile Directed Acyclic Graphical Models

Graphical Dirichlet process.

Bayesian Covariate-Dependent Gaussian Graphical Models with Varying Structure

A new Gaussian graphical model that produces subject-specific and predictive graphs with theoretical guarantee.

Bayesian Hierarchical Quantile Regression with Application to Characterizing the Immune Architecture of Lung Cancer

The successful development and implementation of precision immuno-oncology therapies requires a deeper understanding of the immune architecture at a patient level. T-cell Receptor (TCR) repertoire sequencing is a relatively new technology that …

Causal Discovery with Heterogeneous Observational Data

Discover causality from heterogeneous continuous observational data with directed cyclic graphs.

Bayesian Hierarchical Varying-sparsity Model with Application to Cancer Proteogenomics

A Bayesian hierarchical varying-sparsity regression (BEHAVIOR) model that selects clinically relevant disease markers by integrating proteogenomic and clinical data.

Bayesian Graphical Regression

A new directed acyclic graphical model that produces subject-specific and predictive graphs with theoretical guarantee.