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 201–210 of 2226 papers

TitleStatusHype
Uncertainty Prediction Neural Network (UpNet): Embedding Artificial Neural Network in Bayesian Inversion Framework to Quantify the Uncertainty of Remote Sensing RetrievalCode0
Bayesian Calibration of Win Rate Estimation with LLM EvaluatorsCode0
Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems—0
Compositional simulation-based inference for time seriesCode0
Modelling Alzheimer's Protein Dynamics: A Data-Driven Integration of Stochastic Methods, Machine Learning and Connectome Insights—0
Stein Variational Newton Neural Network EnsemblesCode0
Improving Trust Estimation in Human-Robot Collaboration Using Beta Reputation at Fine-grained TimescalesCode0
Interacting Large Language Model Agents. Interpretable Models and Social Learning—0
Constrained Sampling with Primal-Dual Langevin Monte CarloCode0
Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data CorruptionsCode0
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Benchmark Results

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