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
Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems—0
Subspace Constrained Variational Bayesian Inference for Structured Compressive Sensing with a Dynamic Grid—0
Infinite Ends from Finite Samples: Open-Ended Goal Inference as Top-Down Bayesian Filtering of Bottom-Up Proposals—0
SoftCVI: Contrastive variational inference with self-generated soft labelsCode0
LLMExplainer: Large Language Model based Bayesian Inference for Graph Explanation Generation—0
Variation Bayesian Interference for Multiple Extended Targets or Unresolved Group Targets Tracking—0
Improving Graph Out-of-distribution Generalization on Real-world Data—0
Variational Inference via Smoothed Particle Hydrodynamics—0
Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based ModelsCode0
FedLog: Personalized Federated Classification with Less Communication and More Flexibility—0
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Benchmark Results

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