Adaptively learning the auxiliary ellipse distribution in elliptical slice sampling gives a gradient-free sampler that is ergodic under stated assumptions and empirically competitive with HMC and adaptive random walks on challenging posteriors.
Bayesian inference for logistic models using P
4 Pith papers cite this work. Polarity classification is still indexing.
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PRADAS derives a Bayes-optimal mirror statistic for any splitting scheme, establishes asymptotic FDR control under weak dependence, and optimizes the split ratio as a stopping time to improve power over standard equal-split methods.
JASPER jointly models multi-gene spatial expression via Bayesian basis-function regression, reporting improved detection of spatially varying genes over existing methods on breast cancer data and simulations.
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.
citing papers explorer
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Adaptive Generalized Elliptical Slice Sampling
Adaptively learning the auxiliary ellipse distribution in elliptical slice sampling gives a gradient-free sampler that is ergodic under stated assumptions and empirically competitive with HMC and adaptive random walks on challenging posteriors.
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PRADAS: PRior-Assisted DAta Splitting for False Discovery Rate Control
PRADAS derives a Bayes-optimal mirror statistic for any splitting scheme, establishes asymptotic FDR control under weak dependence, and optimizes the split ratio as a stopping time to improve power over standard equal-split methods.
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JASPER: Joint Bayesian Analysis of Spatial Expression via Regression
JASPER jointly models multi-gene spatial expression via Bayesian basis-function regression, reporting improved detection of spatially varying genes over existing methods on breast cancer data and simulations.
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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.