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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 1531–1540 of 2226 papers

Title	Date	Tasks	Status	Hype
Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy	Sep 13, 2019	AstronomyBayesian Inference	CodeCode Available	0
Learning Bayesian posteriors with neural networks for gravitational-wave inference	Sep 12, 2019	AstronomyBayesian Inference	CodeCode Available	0
Spike Sorting using the Neural Clustering Process	Sep 11, 2019	Bayesian InferenceClustering	CodeCode Available	0
Efficient Bayesian synthetic likelihood with whitening transformations	Sep 11, 2019	Bayesian Inference	—Unverified	0
Correcting Predictions for Approximate Bayesian Inference	Sep 11, 2019	Bayesian InferenceDecision Making	CodeCode Available	0
Evaluating Topic Quality with Posterior Variability	Sep 8, 2019	Bayesian InferenceTopic Models	CodeCode Available	0
Accelerated Information Gradient flow	Sep 4, 2019	Bayesian Inference	CodeCode Available	0
On the Expressiveness of Approximate Inference in Bayesian Neural Networks	Sep 2, 2019	Active LearningBayesian Inference	CodeCode Available	0
Variationally Inferred Sampling Through a Refined Bound for Probabilistic Programs	Aug 26, 2019	Bayesian InferenceDensity Estimation	CodeCode Available	0
Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma	Aug 24, 2019	Bayesian Inference	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