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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 1971–1980 of 2226 papers

Title	Date	Tasks	Status	Hype
Flexible Amortized Variational Inference in qBOLD MRI	Mar 11, 2022	Bayesian InferenceUncertainty Quantification	CodeCode Available	0
Flexible and efficient emulation of spatial extremes processes via variational autoencoders	Jul 16, 2023	Bayesian InferenceGaussian Processes	CodeCode Available	0
Automated Scalable Bayesian Inference via Hilbert Coresets	Oct 13, 2017	Bayesian Inference	CodeCode Available	0
Stochastic Gradient Descent as Approximate Bayesian Inference	Apr 13, 2017	Bayesian Inference	CodeCode Available	0
Bidirectional Convolutional Poisson Gamma Dynamical Systems	Dec 1, 2020	Bayesian InferenceSentence	CodeCode Available	0
Accelerating Convergence of Stein Variational Gradient Descent via Deep Unfolding	Feb 23, 2024	Bayesian InferenceVariational Inference	CodeCode Available	0
Flexible statistical inference for mechanistic models of neural dynamics	Nov 6, 2017	Bayesian Inference	CodeCode Available	0
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap	Feb 9, 2022	Bayesian Inference	CodeCode Available	0
Mutually Regressive Point Processes	Dec 1, 2019	Bayesian InferencePoint Processes	CodeCode Available	0
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming	May 25, 2018	Bayesian InferenceMulti-Armed Bandits	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