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Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 121–130 of 2226 papers

Title	Date	Tasks	Status	Hype
Modeling Neural Activity with Conditionally Linear Dynamical Systems	Feb 25, 2025	Bayesian Inference	CodeCode Available	1
Monte Carlo guided Diffusion for Bayesian linear inverse problems	Aug 15, 2023	Bayesian Inference	CodeCode Available	1
Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive Models	Nov 23, 2022	Bayesian InferenceTime Series	CodeCode Available	1
Neural Variational Gradient Descent	Jul 22, 2021	Bayesian Inferenceregression	CodeCode Available	1
Automatic Posterior Transformation for Likelihood-Free Inference	May 17, 2019	Bayesian InferenceTime Series	CodeCode Available	1
Out-of-Distribution Detection Using Union of 1-Dimensional Subspaces	Jun 19, 2021	Bayesian InferenceOut-of-Distribution Detection	CodeCode Available	1
A Simple Baseline for Bayesian Uncertainty in Deep Learning	Feb 7, 2019	Bayesian InferenceDeep Learning	CodeCode Available	1
Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation	Dec 15, 2023	Bayesian InferenceState Space Models	CodeCode Available	1
A practical tutorial on Variational Bayes	Mar 1, 2021	Bayesian InferenceVariational Inference	CodeCode Available	1
A Primer on Bayesian Neural Networks: Review and Debates	Sep 28, 2023	Bayesian Inference	CodeCode Available	1

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Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	F-SWA	Accuracy	83.61	—	Unverified
2	F-SWAG	Accuracy	80.93	—	Unverified