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 1371–1380 of 2226 papers

TitleStatusHype
Tracking disease outbreaks from sparse data with Bayesian inference—0
Tractable Fully Bayesian Inference via Convex Optimization and Optimal Transport Theory—0
Traffic Flow Prediction via Variational Bayesian Inference-based Encoder-Decoder Framework—0
Trajectory Modeling via Random Utility Inverse Reinforcement Learning—0
Transflow Learning: Repurposing Flow Models Without Retraining—0
Transforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models—0
Transport Gaussian Processes for Regression—0
Transport Quasi-Monte Carlo—0
Tree-Structured Stick Breaking for Hierarchical Data—0
Trust and Time Preference: Measuring a Causal Effect in a Random-Assignment Experiment—0
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Benchmark Results

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