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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 551–560 of 2226 papers

TitleStatusHype
Successive Linear Approximation VBI for Joint Sparse Signal Recovery and Dynamic Grid Parameters EstimationCode1
Flexible and efficient emulation of spatial extremes processes via variational autoencodersCode0
Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review—0
Variational Inference with Gaussian Score MatchingCode1
Variational Prediction—0
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout—0
Bayesian taut splines for estimating the number of modes—0
A generative flow for conditional sampling via optimal transportCode0
Incentive-Theoretic Bayesian Inference for Collaborative Science—0
Uncertainty Informed Optimal Resource Allocation with Gaussian Process based Bayesian Inference—0
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Benchmark Results

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