SOTAVerified

Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 1781–1790 of 2226 papers

TitleStatusHype
Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement LearningCode0
Efficient Bayesian Inference for a Gaussian Process Density Model—0
Kernel embedding of maps for sequential Bayesian inference: The variational mapping particle filterCode0
Wasserstein Variational Inference—0
Semi-Implicit Variational InferenceCode0
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic ProgrammingCode0
Likelihood-free inference with emulator networksCode0
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers—0
Bayesian Inference of Regular Expressions from Human-Generated Example Strings—0
Bayesian posterior approximation via greedy particle optimization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1F-SWAAccuracy83.61—Unverified
2F-SWAGAccuracy80.93—Unverified