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 401–410 of 2226 papers

TitleStatusHype
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data—0
Modeling human decomposition: a Bayesian approach—0
Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier maps—0
Streaming Bayes GFlowNets—0
Bayesian Calibration of Win Rate Estimation with LLM EvaluatorsCode0
Uncertainty Prediction Neural Network (UpNet): Embedding Artificial Neural Network in Bayesian Inversion Framework to Quantify the Uncertainty of Remote Sensing RetrievalCode0
Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems—0
Compositional simulation-based inference for time seriesCode0
Improving Trust Estimation in Human-Robot Collaboration Using Beta Reputation at Fine-grained TimescalesCode0
Modelling Alzheimer's Protein Dynamics: A Data-Driven Integration of Stochastic Methods, Machine Learning and Connectome Insights—0
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Benchmark Results

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