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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 631–640 of 2226 papers

TitleStatusHype
Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping—0
NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse ProblemsCode0
Bayesian Inference on Brain-Computer Interfaces via GLASSCode0
Bayesian Inference for Jump-Diffusion Approximations of Biochemical Reaction Networks—0
Meta-Learned Models of CognitionCode1
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modellingCode1
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry—0
Cascaded Calibration of Mechatronic Systems via Bayesian Inference—0
Robust Outlier Rejection for 3D Registration with Variational BayesCode1
Bayesian neural networks via MCMC: a Python-based tutorialCode1
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Benchmark Results

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