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.
Journal of the American statistical Association , volume=
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A Bayesian data-fusion model combines AI predictions and manual labels from camera traps to yield improved ecological inference and uncertainty quantification for white-tailed deer body condition.
A recursive cubing framework identifies stable hyperparameter regions for MC dropout uncertainty quantification in spatial deep learning and produces competitive or superior predictive intervals versus a statistical baseline on simulations and land-surface temperature data.
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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.
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Improving ecological inference and uncertainty quantification from camera trap data through the fusion of AI confidences and manual annotations
A Bayesian data-fusion model combines AI predictions and manual labels from camera traps to yield improved ecological inference and uncertainty quantification for white-tailed deer body condition.
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A Cubing Strategy for Identifying Stable Hyperparameter Regions for Uncertainty Quantification in Spatial Deep Learning
A recursive cubing framework identifies stable hyperparameter regions for MC dropout uncertainty quantification in spatial deep learning and produces competitive or superior predictive intervals versus a statistical baseline on simulations and land-surface temperature data.