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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 1701–1710 of 2226 papers

TitleStatusHype
Data Subsampling for Bayesian Neural NetworksCode0
Measuring Uncertainty through Bayesian Learning of Deep Neural Network StructureCode0
Debiased Bayesian inference for average treatment effectsCode0
deBInfer: Bayesian inference for dynamical models of biological systems in RCode0
Predictive, scalable and interpretable knowledge tracing on structured domainsCode0
The Deep Weight PriorCode0
Learning Bayesian posteriors with neural networks for gravitational-wave inferenceCode0
Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomyCode0
A Novel Deterministic Framework for Non-probabilistic Recommender SystemsCode0
Simulation-based inference for stochastic nonlinear mixed-effects models with applications in systems biologyCode0
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Benchmark Results

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