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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 1671–1680 of 2226 papers

Title	Date	Tasks	Status	Hype
Predictive Uncertainty Quantification with Compound Density Networks	Feb 4, 2019	Bayesian InferenceUncertainty Quantification	—Unverified	0
Sequential Bayesian Detection of Spike Activities from Fluorescence Observations	Jan 31, 2019	Bayesian Inference	—Unverified	0
Functional Regularisation for Continual Learning with Gaussian Processes	Jan 31, 2019	Bayesian InferenceContinual Learning	CodeCode Available	0
Metric Gaussian Variational Inference	Jan 30, 2019	Bayesian InferenceVariational Inference	CodeCode Available	0
Stochastic Gradient MCMC for Nonlinear State Space Models	Jan 29, 2019	Bayesian InferenceState Space Models	CodeCode Available	0
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets	Jan 28, 2019	Bayesian Inference	CodeCode Available	0
What does the free energy principle tell us about the brain?	Jan 23, 2019	Active LearningBayesian Inference	—Unverified	0
Fitting A Mixture Distribution to Data: Tutorial	Jan 20, 2019	Bayesian InferenceBayesian Optimisation	CodeCode Available	0
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning	Jan 18, 2019	Action UnderstandingBayesian Inference	—Unverified	0
A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data	Jan 18, 2019	Bayesian InferenceBIG-bench Machine Learning	—Unverified	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