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 791–800 of 2226 papers

TitleStatusHype
Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks: Theory, Methods, and Algorithms—0
Addressing Census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements—0
Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty—0
A Bayesian take on option pricing with Gaussian processes—0
Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions—0
A comparison of Bayesian sampling algorithms for high-dimensional particle physics and cosmology applications—0
A Trust-Region Method for Graphical Stein Variational Inference—0
Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas Industry—0
Sparse Bayesian Learning Approach for Discrete Signal Reconstruction—0
Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning—0
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Benchmark Results

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