23 citations · 52 across the 8 of their papers we have counts for
8 papers
Latent variable estimation with composite Hilbert space Gaussian processes
Soham Mukherjee, Javier Enrique Aguilar, Marcello Zago +2
We develop a scalable class of models for latent variable estimation using composite Gaussian processes, with a focus on derivative Gaussian processes. We jointly model multiple da…
R2 priors for Grouped Variance Decomposition in High-dimensional Regression
Javier Enrique Aguilar, David Kohns, Aki Vehtari +1
We introduce the Group-R2 decomposition prior, a hierarchical shrinkage prior that extends R2-based priors to structured regression settings with known groups of predictors. By dec…
Dependency-Aware Shrinkage Priors for High Dimensional Regression
Javier Enrique Aguilar, Paul-Christian Bürkner
In high dimensional regression, global local shrinkage priors have gained significant traction for their ability to yield sparse estimates, improve parameter recovery, and support…
Glucodensity Functional Profiles Outperform Traditional Continuous Glucose Monitoring Metrics
Marcos Matabuena, Rahul Ghosal, Javier Enrique Aguilar +6
Continuous glucose monitoring (CGM) data has revolutionized the management of type 1 diabetes, particularly when integrated with insulin pumps to mitigate clinical events such as h…
Primed Priors for Simulation-Based Validation of Bayesian Models
Luna Fazio, Maximilian Scholz, Javier Enrique Aguilar +1
Simulation-based calibration (SBC) is a method for validating inference algorithms and model implementations through repeated inference on data simulated from a generative model. F…
Generalized Decomposition Priors on R2
Javier Enrique Aguilar, Paul-Christian Bürkner
The adoption of continuous shrinkage priors in high-dimensional linear models has gained widespread attention due to their practical and theoretical advantages. Among them, the R2D…