spBART extends BART by modeling low-dimensional covariates parametrically for interpretability and high-dimensional epigenetic predictors nonparametrically, with a CV-based variable selection procedure, achieving AUC 0.96 on multiple myeloma epigenetic data.
The annals of statistics , pages=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Predictive Bayesian inference posteriors concentrate onto a forward-model-dependent quantity and produce miscalibrated credible sets unless the predictive model contains the true data-generating process.
Introduces a robust OT divergence with stochastic subgradient algorithm and bootstrap-based SBI procedure for parameter inference under joint geometric and TV contamination.
citing papers explorer
-
Semi-Parametric Bayesian Additive Regression Trees for Risk Prediction with High-Dimensional Epigenetic Signatures and Low-Dimensional Covariates
spBART extends BART by modeling low-dimensional covariates parametrically for interpretability and high-dimensional epigenetic predictors nonparametrically, with a CV-based variable selection procedure, achieving AUC 0.96 on multiple myeloma epigenetic data.
-
Concentration and Calibration in Predictive Bayesian Inference
Predictive Bayesian inference posteriors concentrate onto a forward-model-dependent quantity and produce miscalibrated credible sets unless the predictive model contains the true data-generating process.
-
Robust Simulation Based Inference Through Robust Optimal Transport
Introduces a robust OT divergence with stochastic subgradient algorithm and bootstrap-based SBI procedure for parameter inference under joint geometric and TV contamination.