SOTAVerified

Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2191–2200 of 2226 papers

TitleStatusHype
Functional Stochastic Gradient MCMC for Bayesian Neural Networks—0
Functional Variational Inference based on Stochastic Process Generators—0
Function Space Bayesian Pseudocoreset for Bayesian Neural Networks—0
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles—0
Fundamental Linear Algebra Problem of Gaussian Inference—0
Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models—0
GAN priors for Bayesian inference—0
Gaussian Density Parametrization Flow: Particle and Stochastic Approaches—0
Gaussian Measures Conditioned on Nonlinear Observations: Consistency, MAP Estimators, and Simulation—0
Gaussian Process-based Spatial Reconstruction of Electromagnetic fields—0
Show:102550
← PrevPage 220 of 223Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1F-SWAAccuracy83.61—Unverified
2F-SWAGAccuracy80.93—Unverified