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 611–620 of 2226 papers

TitleStatusHype
Biologically Inspired Dynamic Textures for Probing Motion Perception—0
Bridging the reality gap in quantum devices with physics-aware machine learning—0
Bridging the Sim-to-Real Gap with Bayesian Inference—0
Automatic maneuver detection and tracking of space objects in optical survey scenarios based on stochastic hybrid systems formulation—0
Biogeochemistry-Informed Neural Network (BINN) for Improving Accuracy of Model Prediction and Scientific Understanding of Soil Organic Carbon—0
A Unified Kernel for Neural Network Learning—0
Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems—0
Building general Langevin models from discrete data sets—0
Burn-in, bias, and the rationality of anchoring—0
A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting—0
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Benchmark Results

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