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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 1841–1850 of 2226 papers

TitleStatusHype
Riemannian Stein Variational Gradient Descent for Bayesian InferenceCode0
On the use of bootstrap with variational inference: Theory, interpretation, and a two-sample test example—0
Gaussian Process Neurons Learn Stochastic Activation Functions—0
A Parameter-Free Learning Automaton Scheme—0
Computing the quality of the Laplace approximation—0
Variational Bayesian Inference For A Scale Mixture Of Normal Distributions Handling Missing Data—0
A Kolmogorov-Smirnov test for the molecular clock on Bayesian ensembles of phylogenies—0
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models—0
Bootstrapped synthetic likelihood—0
Message Passing Stein Variational Gradient Descent—0
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Benchmark Results

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