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 1551–1560 of 2226 papers

TitleStatusHype
Improving Graph Out-of-distribution Generalization on Real-world Data—0
Incentive-Theoretic Bayesian Inference for Collaborative Science—0
Incoherent Probability Judgments in Large Language Models—0
In-context Exploration-Exploitation for Reinforcement Learning—0
Explaining Emergent In-Context Learning as Kernel Regression—0
In-Context Parametric Inference: Point or Distribution Estimators?—0
Incorporating Bayesian approaches in Deep Learning Research—0
Incorporating the ChEES Criterion into Sequential Monte Carlo Samplers—0
Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing—0
Inference in continuous-time change-point models—0
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Benchmark Results

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