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 571–580 of 2226 papers

TitleStatusHype
Variational inference of fractional Brownian motion with linear computational complexity—0
ABC random forests for Bayesian parameter inference—0
Can Sequential Bayesian Inference Solve Continual Learning?—0
Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution—0
Automatic Variational Inference in Stan—0
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning—0
Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation—0
Calibration of Model Uncertainty for Dropout Variational Inference—0
Automatic maneuver detection and tracking of space objects in optical survey scenarios based on stochastic hybrid systems formulation—0
Automatic Bayesian Density Analysis—0
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Benchmark Results

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