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Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 1521–1530 of 2226 papers

TitleStatusHype
Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints—0
How Adults Understand What Young Children Say—0
How Good is the Bayes Posterior in Deep Neural Networks Really?—0
How Much More Probable is "Much More Probable"? Verbal Expressions for Probability Updates—0
How particular is the physics of the free energy principle?—0
How To Make Your Cell Tracker Say "I dunno!"—0
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models—0
Human collective intelligence as distributed Bayesian inference—0
Human Goal Recognition as Bayesian Inference: Investigating the Impact of Actions, Timing, and Goal Solvability—0
Human Inference in Changing Environments With Temporal Structure—0
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Benchmark Results

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