SOTAVerified

Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 931–940 of 2226 papers

TitleStatusHype
A Two-stage Multiband WiFi Sensing Scheme via Stochastic Particle-Based Variational Bayesian Inference—0
Minimum Description Length Control—0
Mean-field Variational Inference via Wasserstein Gradient Flow—0
Neural Posterior Estimation with Differentiable Simulators—0
Latent Variable Models for Bayesian Causal Discovery—0
Comparative Study of Inference Methods for Interpolative Decomposition—0
Towards Unifying Perceptual Reasoning and Logical Reasoning—0
Bayesian Neural Network Detector for an Orthogonal Time Frequency Space Modulation—0
Variational Bayesian inference for CP tensor completion with side information—0
Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport—0
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Benchmark Results

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