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Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 1291–1300 of 2226 papers

TitleStatusHype
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference—0
Developing and Testing a Bayesian Analysis of Fluorescence Lifetime Measurements—0
Development of Bayesian Component Failure Models in E1 HEMP Grid Analysis—0
Device Detection and Channel Estimation in MTC with Correlated Activity Pattern—0
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs—0
Diagnosing model misspecification and performing generalized Bayes' updates via probabilistic classifiers—0
Differentially Private Bayesian Inference for Generalized Linear Models—0
Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty—0
Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions—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
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Benchmark Results

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