Introduces tangential Bayes denoiser for Riemannian Gaussian mixtures on manifolds via spectral Laplace-Beltrami approximation, with nearly Bayes risk in low noise and minimax optimality on the circle.
Journal of Applied Statistics , year = 2024, month = feb, volume =
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A 4.5σ excess RM dispersion of 4.13 ± 0.91 rad m^{-2} is found in 191 Mg II sightlines versus controls, implying model-dependent CGM magnetic fields of 0.4-0.8 μG at projected radii 20-150 kpc and z~1.14.
Negative correlations between biomarkers maximize the combined AUC in multivariate normal models, with the largest gains when markers have equal predictive power, as shown by derivation, simulations, and pancreatic cancer metabolite data.
citing papers explorer
-
Nonparametric Riemannian Empirical Bayes, and Denoising Measurements on Manifolds
Introduces tangential Bayes denoiser for Riemannian Gaussian mixtures on manifolds via spectral Laplace-Beltrami approximation, with nearly Bayes risk in low noise and minimax optimality on the circle.
-
Magnetised CGM Gas at z~1 revealed by SPICE-RACS
A 4.5σ excess RM dispersion of 4.13 ± 0.91 rad m^{-2} is found in 191 Mg II sightlines versus controls, implying model-dependent CGM magnetic fields of 0.4-0.8 μG at projected radii 20-150 kpc and z~1.14.
-
Evaluating the role of correlation among markers in prediction models
Negative correlations between biomarkers maximize the combined AUC in multivariate normal models, with the largest gains when markers have equal predictive power, as shown by derivation, simulations, and pancreatic cancer metabolite data.