SOTAVerified

Bayesian Inference

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

Papers

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Showing 381–390 of 2226 papers

TitleStatusHype
Adaptive Nonparametric Perturbations of Parametric Bayesian ModelsCode0
BayesAdapter: enhanced uncertainty estimation in CLIP few-shot adaptation—0
Prediction of Occluded Pedestrians in Road Scenes using Human-like Reasoning: Insights from the OccluRoads Dataset—0
Hardware implementation of timely reliable Bayesian decision-making using memristors—0
DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural Anomaly Detection—0
The Polynomial Stein Discrepancy for Assessing Moment ConvergenceCode0
Semiparametric Bayesian Difference-in-Differences—0
Path-Guided Particle-based SamplingCode0
MACAW: A Causal Generative Model for Medical ImagingCode0
Extension of compressive sampling to binary vector recovery for model-based defect imaging—0
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Benchmark Results

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