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Bayesian Inference

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

Papers

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Showing 171–180 of 2226 papers

TitleStatusHype
A partial likelihood approach to tree-based density modeling and its application in Bayesian inference—0
Information-Geometric Barycenters for Bayesian Federated Learning—0
Bayesian inference of mean velocity fields and turbulence models from flow MRI—0
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
The Polynomial Stein Discrepancy for Assessing Moment ConvergenceCode0
DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural Anomaly Detection—0
Semiparametric Bayesian Difference-in-Differences—0
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Benchmark Results

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