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 881–890 of 2226 papers

TitleStatusHype
Efficient Weight-Space Laplace-Gaussian Filtering and Smoothing for Sequential Deep Learning—0
EinSteinVI: General and Integrated Stein Variational Inference—0
Efficient Approximate Inference with Walsh-Hadamard Variational Inference—0
Bayesian Inference for Structured Spike and Slab Priors—0
A Parzen-based distance between probability measures as an alternative of summary statistics in Approximate Bayesian Computation—0
Elements of Sequential Monte Carlo—0
Eliciting Language Model Behaviors with Investigator Agents—0
Embarrassingly parallel MCMC using deep invertible transformations—0
Embedded Nonlocal Operator Regression (ENOR): Quantifying model error in learning nonlocal operators—0
Efficient acquisition rules for model-based approximate Bayesian computation—0
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Benchmark Results

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