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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 1481–1490 of 2226 papers

Title	Date	Tasks	Status	Hype
Why bigger is not always better: on finite and infinite neural networks	Oct 17, 2019	Bayesian InferenceRepresentation Learning	—Unverified	0
Efficient Bayesian Inference for Nested Simulators	Oct 16, 2019	Bayesian Inference	—Unverified	0
Graph Tracking in Dynamic Probabilistic Programs via Source Transformations	Oct 16, 2019	Bayesian InferenceBIG-bench Machine Learning	—Unverified	0
Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes	Oct 16, 2019	Bayesian InferenceGaussian Processes	—Unverified	0
Bijectors.jl: Flexible transformations for probability distributions	Oct 16, 2019	Bayesian InferenceVariational Inference	CodeCode Available	0
Neural Permutation Processes	Oct 16, 2019	Bayesian Inference	—Unverified	0
Gaussian Process Meta-Representations For Hierarchical Neural Network Weight Priors	Oct 16, 2019	Active LearningBayesian Inference	—Unverified	0
Bayesian Model Selection for Identifying Markov Equivalent Causal Graphs	Oct 16, 2019	Bayesian InferenceCausal Discovery	—Unverified	0
Stein Variational Gradient Descent for Approximate Bayesian Computation	Oct 16, 2019	Bayesian InferenceVariational Inference	—Unverified	0
Variationally Inferred Sampling Through a Refined Bound	Oct 16, 2019	Bayesian InferenceDensity Estimation	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