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 1921–1930 of 2226 papers

TitleStatusHype
Bridging the Sim-to-Real Gap with Bayesian Inference—0
B-SCST: Bayesian Self-Critical Sequence Training for Image Captioning—0
Building fast Bayesian computing machines out of intentionally stochastic, digital parts—0
Building general Langevin models from discrete data sets—0
Burn-in, bias, and the rationality of anchoring—0
Calibrating Agent-based Models to Microdata with Graph Neural Networks—0
Calibration and Filtering of Exponential L\'evy Option Pricing Models—0
Calibration and Uncertainty Quantification of Bayesian Convolutional Neural Networks for Geophysical Applications—0
Calibration of Model Uncertainty for Dropout Variational Inference—0
Can Bayesian Neural Networks Make Confident Predictions?—0
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Benchmark Results

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