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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 1731–1740 of 2226 papers

Title	Date	Tasks	Status	Hype
Variational Inference with Continuously-Indexed Normalizing Flows	Jul 10, 2020	Bayesian InferenceDensity Estimation	CodeCode Available	0
Learning Summary Statistics for Bayesian Inference with Autoencoders	Jan 28, 2022	Bayesian InferenceDecoder	CodeCode Available	0
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks	May 30, 2019	Bayesian InferenceMeta-Learning	CodeCode Available	0
Deep Neural Networks as Gaussian Processes	Nov 1, 2017	Bayesian InferenceGaussian Processes	CodeCode Available	0
SNAPE-PM: Building and Utilizing Dynamic Partner Models for Adaptive Explanation Generation	May 19, 2025	Bayesian InferenceDecision Making	CodeCode Available	0
Bayesian Online Prediction of Change Points	Feb 12, 2019	Bayesian InferenceChange Point Detection	CodeCode Available	0
Undirected Graphical Models as Approximate Posteriors	Jan 11, 2019	Bayesian Inference	CodeCode Available	0
On out-of-distribution detection with Bayesian neural networks	Oct 12, 2021	Bayesian InferenceGaussian Processes	CodeCode Available	0
Deep surrogate accelerated delayed-acceptance HMC for Bayesian inference of spatio-temporal heat fluxes in rotating disc systems	Apr 5, 2022	Bayesian Inference	CodeCode Available	0
Demonstrating the Continual Learning Capabilities and Practical Application of Discrete-Time Active Inference	Sep 30, 2024	Bayesian InferenceContinual Learning	CodeCode Available	0

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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