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 291–300 of 2226 papers

TitleStatusHype
A Parameter-Free Learning Automaton Scheme—0
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes—0
Bayesian Critique-Tune-Based Reinforcement Learning with Adaptive Pressure for Multi-Intersection Traffic Signal Control—0
An Unsupervised Deep Learning Approach for the Wave Equation Inverse Problem—0
An uncertainty-aware Digital Shadow for underground multimodal CO2 storage monitoring—0
A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models—0
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—0
A Hamilton-Jacobi Reachability-Based Framework for Predicting and Analyzing Human Motion for Safe Planning—0
Bayesian data fusion with shared priors—0
Bayesian deep learning framework for uncertainty quantification in high dimensions—0
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Benchmark Results

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