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
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model—0
Object based Bayesian full-waveform inversion for shear elastography—0
The Compositional Structure of Bayesian Inference—0
CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulatorsCode1
Location Tracking for Reconfigurable Intelligent Surfaces Aided Vehicle Platoons: Diverse Sparsities Inspired Approaches—0
Bayesian Over-the-Air FedAvg via Channel Driven Stochastic Gradient Langevin Dynamics—0
Variational Nonlinear Kalman Filtering with Unknown Process Noise Covariance—0
A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes—0
Inferential Moments of Uncertain Multivariable Systems—0
Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss InformationCode0
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Benchmark Results

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