Bayesian Multi-Group Gaussian Process Models for Heterogeneous Group-Structured Data.
Didong Li, Andrew Jones, Sudipto Banerjee, Barbara E. Engelhardt · PubMed · 2025
is a finite set representing the group label, by developing general classes of valid (positive definite) covariance functions on such domains. MGGPs are able to accurately recover relationships between the groups and efficiently share strength across samples from all groups during inference, while capturing distinct group-specific behaviors in the conditional posterior distributions. We demonstrate inference in MGGPs through simulation experiments, and we apply our proposed MGGP regression framework to gene expression data to illustrate the behavior and enhanced inferential capabilities of multi-group Gaussian processes by jointly modeling continuous and categorical variables.