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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 1771–1780 of 2226 papers

Title	Date	Tasks	Status	Hype
Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement Learning	May 29, 2018	Bayesian Inferencereinforcement-learning	CodeCode Available	0
Semi-Implicit Variational Inference	May 28, 2018	Bayesian InferenceVariational Inference	CodeCode Available	0
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming	May 25, 2018	Bayesian InferenceMulti-Armed Bandits	CodeCode Available	0
Likelihood-free inference with emulator networks	May 23, 2018	Bayesian Inference	CodeCode Available	0
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers	May 23, 2018	Bayesian InferenceState Space Models	—Unverified	0
Bayesian Inference of Regular Expressions from Human-Generated Example Strings	May 22, 2018	Bayesian Inference	—Unverified	0
Bayesian posterior approximation via greedy particle optimization	May 21, 2018	Bayesian InferenceVariational Inference	—Unverified	0
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy	May 20, 2018	Bayesian InferenceDeep Learning	CodeCode Available	0
Nonparametric Bayesian Deep Networks with Local Competition	May 19, 2018	Bayesian Inference	CodeCode Available	0
Approximate Bayesian inference in spatial environments	May 18, 2018	Bayesian InferenceDecision Making	—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