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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 1191–1200 of 2226 papers

TitleStatusHype
Safe-Bayesian Generalized Linear Regression—0
Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis—0
Salient Structure Detection by Context-Guided Visual Search—0
Sampling-based Bayesian Inference with gradient uncertainty—0
Sampling-based probabilistic inference emerges from learning in neural circuits with a cost on reliability—0
Sampling Bias Correction for Supervised Machine Learning: A Bayesian Inference Approach with Practical Applications—0
Sampling constrained probability distributions using Spherical Augmentation—0
Sampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small dataset—0
Scalable Bayesian inference for the generalized linear mixed model—0
Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks—0
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Benchmark Results

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