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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 1281–1290 of 2226 papers

TitleStatusHype
Tracking disease outbreaks from sparse data with Bayesian inference—0
Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient DescentCode1
Non-linear fitting with joint spatial regularization in Arterial Spin Labeling—0
Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using The Generalized Hyperbolic Prior—0
FedBE: Making Bayesian Model Ensemble Applicable to Federated LearningCode1
Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution SettingsCode0
Bayesian Perceptron: Towards fully Bayesian Neural Networks—0
β-Cores: Robust Large-Scale Bayesian Data Summarization in the Presence of OutliersCode0
The linear conditional expectation in Hilbert space—0
Surrogate Model For Field Optimization Using Beta-VAE Based Regression—0
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Benchmark Results

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