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 821–830 of 2226 papers

TitleStatusHype
Efficient Reinforcement Learning with Large Language Model Priors—0
Divide, Conquer, Combine Bayesian Decision Tree Sampling—0
Bayesian Inference for Gaussian Process Classifiers with Annealing and Pseudo-Marginal MCMC—0
Do Bayesian Neural Networks Improve Weapon System Predictive Maintenance?—0
A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach—0
Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluation—0
Efficient Weight-Space Laplace-Gaussian Filtering and Smoothing for Sequential Deep Learning—0
Domain Agnostic Conditional Invariant Predictions for Domain Generalization—0
Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference—0
E-PINNs: Epistemic Physics-Informed Neural Networks—0
Show:102550
← PrevPage 83 of 223Next →

Benchmark Results

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