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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 1181–1190 of 2226 papers

TitleStatusHype
Data augmentation in Bayesian neural networks and the cold posterior effect—0
An Interpretable Neural Network for Parameter Inference—0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
Bayesian Boosting for Linear Mixed Models—0
Nonlinear Hawkes Processes in Time-Varying System—0
Bridging physiological and perceptual views of autism by means of sampling-based Bayesian inference—0
Context-tree weighting for real-valued time series: Bayesian inference with hierarchical mixture models—0
Knowing when we do not know: Bayesian continual learning for sensing-based analysis tasks—0
Canonical Cortical Circuits and the Duality of Bayesian Inference and Optimal Control—0
Bayesian Inference for Gamma Models—0
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Benchmark Results

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