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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 1441–1450 of 2226 papers

TitleStatusHype
Frequentist Guarantees of Distributed (Non)-Bayesian Inference—0
Semantic Information G Theory and Logical Bayesian Inference for Machine Learning—0
From Shannon's Channel to Semantic Channel via New Bayes' Formulas for Machine Learning—0
From the Expectation Maximisation Algorithm to Autoencoded Variational Bayes—0
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations—0
Fully Bayesian inference for neural models with negative-binomial spiking—0
Functional Distributional Semantics—0
Functional Space Variational Inference for Uncertainty Estimation in Computer Aided Diagnosis—0
Functional Stochastic Gradient MCMC for Bayesian Neural Networks—0
Functional Variational Inference based on Stochastic Process Generators—0
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Benchmark Results

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