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 1021–1030 of 2226 papers

TitleStatusHype
Semantic Information G Theory and Logical Bayesian Inference for Machine Learning—0
From Shannon's Channel to Semantic Channel via New Bayes' Formulas for Machine Learning—0
Distribution learning via neural differential equations: minimal energy regularization and approximation theory—0
Distributionally Robust Optimisation with Bayesian Ambiguity Sets—0
Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review—0
Distributed Variational Bayesian Algorithms Over Sensor Networks—0
A Novel Gaussian Process Based Ground Segmentation Algorithm with Local-Smoothness Estimation—0
A deep learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generation—0
Accelerating a hybrid continuum-atomistic fluidic model with on-the-fly machine learning—0
Distributed Bayesian Inference for Large-Scale IoT Systems—0
Show:102550
← PrevPage 103 of 223Next →

Benchmark Results

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