Four new acquisition functions are developed for Bayesian quadrature to measure and reduce prediction uncertainties in posterior and evidence estimation, extended to transitional schemes for robust performance on complex posteriors.
arXiv preprint arXiv:2103.01327 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
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.
POTTERS extends the Potts model with generalized spatial dependence and external priors for Bayesian remote sensing image segmentation via variational inference, without needing target-region labels.
citing papers explorer
-
Bayesian Active Learning for Bayesian Model Updating: the Art of Acquisition Functions and Beyond
Four new acquisition functions are developed for Bayesian quadrature to measure and reduce prediction uncertainties in posterior and evidence estimation, extended to transitional schemes for robust performance on complex posteriors.
-
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.
-
Scalable Bayesian Spatial Mixture Modelling for Remote Sensing Image Segmentation
POTTERS extends the Potts model with generalized spatial dependence and external priors for Bayesian remote sensing image segmentation via variational inference, without needing target-region labels.