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 621–630 of 2226 papers

TitleStatusHype
Calibrating Agent-based Models to Microdata with Graph Neural Networks—0
Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution—0
A Markov Model of Machine Translation using Non-parametric Bayesian Inference—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
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference—0
Canonical Cortical Circuits and the Duality of Bayesian Inference and Optimal Control—0
Confidence in Large Language Model Evaluation: A Bayesian Approach to Limited-Sample Challenges—0
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Benchmark Results

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