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 681–690 of 2226 papers

TitleStatusHype
Confidence Estimation via Sequential Likelihood Mixing—0
Confidence in Large Language Model Evaluation: A Bayesian Approach to Limited-Sample Challenges—0
Confident in the Crowd: Bayesian Inference to Improve Data Labelling in Crowdsourcing—0
Conjugate Natural Selection—0
Connections between sequential Bayesian inference and evolutionary dynamics—0
Consciousness is entailed by compositional learning of new causal structures in deep predictive processing systems—0
A majorization-minimization algorithm for nonnegative binary matrix factorization—0
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck—0
BI-EqNO: Generalized Approximate Bayesian Inference with an Equivariant Neural Operator Framework—0
Bidirectional Recurrent Neural Networks as Generative Models—0
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Benchmark Results

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