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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 1791–1800 of 2226 papers

TitleStatusHype
Conditionally Independent Multiresolution Gaussian ProcessesCode0
Logistic Variational Bayes RevisitedCode0
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopyCode0
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High DimensionsCode0
Do Bayesian Variational Autoencoders Know What They Don't Know?Code0
Projected Wasserstein gradient descent for high-dimensional Bayesian inferenceCode0
Conditional diffusions for amortized neural posterior estimationCode0
Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive ModelsCode0
Uncertainty in Neural Networks: Approximately Bayesian EnsemblingCode0
Data-driven Modeling and Inference for Bayesian Gaussian Process ODEs via Double Normalizing FlowsCode0
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Benchmark Results

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