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 1051–1060 of 2226 papers

TitleStatusHype
A Bayesian/Information Theoretic Model of Bias Learning—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
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC—0
Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions—0
Bayesian Inference Accelerator for Spiking Neural Networks—0
Annealing Flow Generative Models Towards Sampling High-Dimensional and Multi-Modal Distributions—0
Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty—0
Bayesian Incremental Inference Update by Re-using Calculations from Belief Space Planning: A New Paradigm—0
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
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Benchmark Results

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