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 861–870 of 2226 papers

TitleStatusHype
On the Convergence of the Shapley Value in Parametric Bayesian Learning GamesCode0
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems—0
Addressing Census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements—0
Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking—0
Sequential Importance Sampling for Hybrid Model Bayesian Inference to Support Bioprocess Mechanism Learning and Robust Control—0
A Deep Learning Approach to Dst Index Prediction—0
Bézier Curve Gaussian Processes—0
TopWORDS-Seg: Simultaneous Text Segmentation and Word Discovery for Open-Domain Chinese Texts via Bayesian Inference—0
A Dataset-free Deep learning Method for Low-Dose CT Image Reconstruction—0
Approximating Permutations with Neural Network Components for Travelling Photographer Problem—0
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Benchmark Results

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