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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 511–520 of 2226 papers

TitleStatusHype
Verbalized Probabilistic Graphical Modeling with Large Language Models—0
Stochastic full waveform inversion with deep generative prior for uncertainty quantification—0
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians—0
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation—0
Reparameterization invariance in approximate Bayesian inference—0
Event-horizon-scale Imaging of M87* under Different Assumptions via Deep Generative Image Priors—0
Development of Bayesian Component Failure Models in E1 HEMP Grid Analysis—0
Is In-Context Learning in Large Language Models Bayesian? A Martingale PerspectiveCode0
Logistic Variational Bayes RevisitedCode0
Information limits and Thouless-Anderson-Palmer equations for spiked matrix models with structured noiseCode0
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Benchmark Results

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