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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 41–50 of 2226 papers

TitleStatusHype
Bayesian differential programming for robust systems identification under uncertaintyCode1
A Bit More Bayesian: Domain-Invariant Learning with UncertaintyCode1
BayesianFitForecast: A User-Friendly R Toolbox for Parameter Estimation and Forecasting with Ordinary Differential EquationsCode1
Bayesian continual learning and forgetting in neural networksCode1
Accelerated Bayesian SED Modeling using Amortized Neural Posterior EstimationCode1
Understanding and Accelerating Particle-Based Variational InferenceCode1
Bayesian Diffusion Models for 3D Shape ReconstructionCode1
Variational multiple shooting for Bayesian ODEs with Gaussian processesCode1
Bayes-Newton Methods for Approximate Bayesian Inference with PSD GuaranteesCode1
BayesDLL: Bayesian Deep Learning LibraryCode1
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Benchmark Results

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