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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 1451–1460 of 2226 papers

TitleStatusHype
Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon SynapseCode0
Mutually Regressive Point ProcessesCode0
AGEM: Solving Linear Inverse Problems via Deep Priors and SamplingCode0
Efficient Approximate Inference with Walsh-Hadamard Variational Inference—0
Transflow Learning: Repurposing Flow Models Without Retraining—0
A Multilayered Block Network Model to Forecast Large Dynamic Transportation Graphs: an Application to US Air Transport—0
Learning of Weighted Multi-layer Networks via Dynamic Social Spaces, with Application to Financial Interbank TransactionsCode0
Differentially Private Federated Variational InferenceCode0
Measuring Uncertainty through Bayesian Learning of Deep Neural Network StructureCode0
Iterative Construction of Gaussian Process Surrogate Models for Bayesian Inference—0
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Benchmark Results

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