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 191–200 of 2226 papers

TitleStatusHype
Bayesian Uncertainty for Gradient Aggregation in Multi-Task LearningCode1
PyVBMC: Efficient Bayesian inference in PythonCode1
A new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection areaCode1
Reactive Message Passing for Scalable Bayesian InferenceCode1
Reliable amortized variational inference with physics-based latent distribution correctionCode1
Marginal Post Processing of Bayesian Inference Products with Normalizing Flows and Kernel Density EstimatorsCode1
RNN with Particle Flow for Probabilistic Spatio-temporal ForecastingCode1
A Framework for Improving the Reliability of Black-box Variational InferenceCode1
GAN-based Priors for Quantifying UncertaintyCode1
πVAE: a stochastic process prior for Bayesian deep learning with MCMCCode1
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Benchmark Results

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