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
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale—0
Accelerating MCMC via Parallel Predictive Prefetching—0
Bayesian inference of a new Mallows model for characterising symptom sequences applied in primary progressive aphasia—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
Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference—0
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Benchmark Results

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