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 131–140 of 2226 papers

TitleStatusHype
Posterior SBC: Simulation-Based Calibration Checking Conditional on DataCode0
Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation—0
Optimal Subspace Inference for the Laplace Approximation of Bayesian Neural NetworksCode0
Constrained belief updates explain geometric structures in transformer representations—0
Eliciting Language Model Behaviors with Investigator Agents—0
Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems—0
Biogeochemistry-Informed Neural Network (BINN) for Improving Accuracy of Model Prediction and Scientific Understanding of Soil Organic Carbon—0
Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised LearningCode0
VKFPos: A Learning-Based Monocular Positioning with Variational Bayesian Extended Kalman Filter IntegrationCode0
BARNN: A Bayesian Autoregressive and Recurrent Neural Network—0
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Benchmark Results

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