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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 1371–1380 of 2226 papers

TitleStatusHype
The Two Kinds of Free Energy and the Bayesian RevolutionCode0
Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidence—0
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck—0
Practical calibration of the temperature parameter in Gibbs posteriorsCode0
Gaussian Process Learning-based Probabilistic Optimal Power Flow—0
A Practical Introduction to Bayesian Estimation of Causal Effects: Parametric and Nonparametric ApproachesCode1
Bayesian differential programming for robust systems identification under uncertaintyCode1
Bayesian Consensus: Consensus Estimates from Miscalibrated Instruments under Heteroscedastic Noise—0
Scaling Bayesian inference of mixed multinomial logit models to very large datasets—0
Model Uncertainty Quantification for Reliable Deep Vision Structural Health Monitoring—0
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Benchmark Results

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