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 881–890 of 2226 papers

TitleStatusHype
Data Subsampling for Bayesian Neural NetworksCode0
Marginalized particle Gibbs for multiple state-space models coupled through shared parameters—0
On Divergence Measures for Bayesian PseudocoresetsCode0
Sampling-based inference for large linear models, with application to linearised LaplaceCode0
Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion ModelsCode0
Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks—0
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness—0
Robust Bayesian Inference for Moving Horizon Estimation—0
Uncertainty-Aware Meta-Learning for Multimodal Task DistributionsCode0
Adaptive Synaptic Failure Enables Sampling from Posterior Predictive Distributions in the Brain—0
Show:102550
← PrevPage 89 of 223Next →

Benchmark Results

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