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 931–940 of 2226 papers

TitleStatusHype
Excess risk analysis for epistemic uncertainty with application to variational inference—0
Exchangeable Variational Autoencoders with Applications to Genomic Data—0
Exoplanet Characterization using Conditional Invertible Neural Networks—0
Bayesian Inference of Transition Matrices from Incomplete Graph Data with a Topological Prior—0
Expectation Propagation performs a smoothed gradient descent—0
Experimentally detecting a quantum change point via Bayesian inference—0
Explainable Lane Change Prediction for Near-Crash Scenarios Using Knowledge Graph Embeddings and Retrieval Augmented Generation—0
Exploiting Dynamic Sparsity for Near-Field Spatial Non-Stationary XL-MIMO Channel Tracking—0
A Tractable Fully Bayesian Method for the Stochastic Block Model—0
A time-varying finance-led model for U.S. business cycles—0
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Benchmark Results

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