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 1381–1390 of 2226 papers

TitleStatusHype
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift—0
The principles of adaptation in organisms and machines II: Thermodynamics of the Bayesian brain—0
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal PosteriorsCode0
Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference—0
Calibration of Model Uncertainty for Dropout Variational Inference—0
Learning to infer in recurrent biological networksCode0
Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF—0
Solving Differential Equations Using Neural Network Solution Bundles—0
Hidden Markov Models Applied To Intraday Momentum Trading With Side Information—0
Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference—0
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Benchmark Results

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