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 941–950 of 2226 papers

TitleStatusHype
Exploring Action-Centric Representations Through the Lens of Rate-Distortion Theory—0
Bayesian inference on random simple graphs with power law degree distributions—0
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling—0
A theory of representation learning gives a deep generalisation of kernel methods—0
Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference—0
Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidence—0
Extending the statistical software package Engine for Likelihood-Free Inference—0
Extension of compressive sampling to binary vector recovery for model-based defect imaging—0
Factorized Asymptotic Bayesian Inference for Factorial Hidden Markov Models—0
A Tractable Fully Bayesian Method for the Stochastic Block Model—0
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Benchmark Results

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