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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 1841–1850 of 2226 papers

TitleStatusHype
Variationally Inferred Sampling Through a Refined BoundCode0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian InferenceCode0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
A Metalearned Neural Circuit for Nonparametric Bayesian InferenceCode0
The Two Kinds of Free Energy and the Bayesian RevolutionCode0
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard ModelCode0
Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution SettingsCode0
Random-effects substitution models for phylogenetics via scalable gradient approximationsCode0
Random Feature Stein DiscrepanciesCode0
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Benchmark Results

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