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.
Bayesian transfer learning: An overview of probabilistic graphical models for transfer learning
2 Pith papers cite this work. Polarity classification is still indexing.
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Bayesian transfer learning is added to UKF and CKF so that parameters from a low-noise source sensor improve estimates in a high-noise primary sensor, with simulations showing better performance than isolated filters or measurement fusion.
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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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Object Tracking Incorporating Transfer Learning into Unscented and Cubature Kalman Filters
Bayesian transfer learning is added to UKF and CKF so that parameters from a low-noise source sensor improve estimates in a high-noise primary sensor, with simulations showing better performance than isolated filters or measurement fusion.