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Bayesian Inference

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

Papers

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Showing 21–30 of 2226 papers

TitleStatusHype
Simulation-Based Inference for Global Health DecisionsCode2
Bayesian continual learning and forgetting in neural networksCode1
A friendly introduction to triangular transportCode1
From Theory to Application: A Practical Introduction to Neural Operators in Scientific ComputingCode1
Modeling Neural Activity with Conditionally Linear Dynamical SystemsCode1
TimePFN: Effective Multivariate Time Series Forecasting with Synthetic DataCode1
VINP: Variational Bayesian Inference with Neural Speech Prior for Joint ASR-Effective Speech Dereverberation and Blind RIR IdentificationCode1
Can Transformers Learn Full Bayesian Inference in Context?Code1
BayesianFitForecast: A User-Friendly R Toolbox for Parameter Estimation and Forecasting with Ordinary Differential EquationsCode1
Scalable Random Feature Latent Variable ModelsCode1
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Benchmark Results

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