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 1191–1200 of 2226 papers

TitleStatusHype
Separated-Spectral-Distribution Estimation Based on Bayesian Inference with Single RGB Camera—0
Online Bayesian inference for multiple changepoints and risk assessment—0
Confident in the Crowd: Bayesian Inference to Improve Data Labelling in Crowdsourcing—0
Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC—0
Calibration and Uncertainty Quantification of Bayesian Convolutional Neural Networks for Geophysical Applications—0
Trajectory Modeling via Random Utility Inverse Reinforcement Learning—0
How particular is the physics of the free energy principle?—0
Vector autoregression models with skewness and heavy tails—0
Privacy Amplification Via Bernoulli Sampling—0
Scalable Quasi-Bayesian Inference for Instrumental Variable Regression—0
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Benchmark Results

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