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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 2041–2050 of 2226 papers

Title	Date	Tasks	Status	Hype
Approximate Inference with the Variational Holder Bound	Jun 19, 2015	Bayesian InferenceNumerical Integration	—Unverified	0
Dependent Multinomial Models Made Easy: Stick Breaking with the Pólya-Gamma Augmentation	Jun 18, 2015	Bayesian InferencePosition	CodeCode Available	0
Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases	Jun 18, 2015	Additive modelsBayesian Inference	CodeCode Available	0
Learning Deep Generative Models with Doubly Stochastic MCMC	Jun 15, 2015	Bayesian InferenceDensity Estimation	—Unverified	0
Automatic Variational Inference in Stan	Jun 10, 2015	Bayesian InferenceProbabilistic Programming	—Unverified	0
Parallelizing MCMC with Random Partition Trees	Jun 10, 2015	Bayesian Inference	CodeCode Available	0
Provable Bayesian Inference via Particle Mirror Descent	Jun 9, 2015	Bayesian InferenceGaussian Processes	—Unverified	0
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families	Jun 8, 2015	Bayesian Inference	CodeCode Available	0
Variational Dropout and the Local Reparameterization Trick	Jun 8, 2015	Bayesian Inference	CodeCode Available	1
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning	Jun 6, 2015	Bayesian InferenceDeep Reinforcement Learning	CodeCode Available	1

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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