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 1931–1940 of 2226 papers

TitleStatusHype
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
Can Sequential Bayesian Inference Solve Continual Learning?—0
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning—0
Cascaded Calibration of Mechatronic Systems via Bayesian Inference—0
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models—0
Causal Inference through a Witness Protection Program—0
Predictive Coding beyond Correlations—0
Challenges and Opportunities in Approximate Bayesian Deep Learning for Intelligent IoT Systems—0
Chance, long tails, and inference: a non-Gaussian, Bayesian theory of vocal learning in songbirds—0
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Benchmark Results

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