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 1061–1070 of 2226 papers

TitleStatusHype
Differentially Private Bayesian Inference for Generalized Linear Models—0
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie—0
Diagnosing model misspecification and performing generalized Bayes' updates via probabilistic classifiers—0
An Introduction to Animal Movement Modeling with Hidden Markov Models using Stan for Bayesian Inference—0
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs—0
Device Detection and Channel Estimation in MTC with Correlated Activity Pattern—0
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution—0
Development of Bayesian Component Failure Models in E1 HEMP Grid Analysis—0
Bayesian Hypernetworks—0
An Interpretable Neural Network for Parameter Inference—0
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Benchmark Results

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