9 citations · 18 across the 8 of their papers we have counts for
5 papers · 1 filter
Multi-group Gaussian Processes
Didong Li, Andrew Jones, Sudipto Banerjee +1
Gaussian processes (GPs) are pervasive in functional data analysis, machine learning, and spatial statistics for modeling complex dependencies. Modern scientific data sets are typi…
Nonnegative spatial factorization
F. William Townes, Barbara E. Engelhardt
Gaussian processes are widely used for the analysis of spatial data due to their nonparametric flexibility and ability to quantify uncertainty, and recently developed scalable appr…
Contrastive latent variable modeling with application to case-control sequencing experiments
Andrew Jones, F. William Townes, Didong Li +1
High-throughput RNA-sequencing (RNA-seq) technologies are powerful tools for understanding cellular state. Often it is of interest to quantify and summarize changes in cell state t…
Probabilistic Contrastive Principal Component Analysis
Didong Li, Andrew Jones, Barbara Engelhardt
Dimension reduction is useful for exploratory data analysis. In many applications, it is of interest to discover variation that is enriched in a "foreground" dataset relative to a…
Bayesian Ordinal Quantile Regression with a Partially Collapsed Gibbs Sampler
Isabella N Grabski, Roberta De Vito, Barbara E Engelhardt
Unlike standard linear regression, quantile regression captures the relationship between covariates and the conditional response distribution as a whole, rather than only the relat…