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 591–600 of 2226 papers

TitleStatusHype
A Mathematical Walkthrough and Discussion of the Free Energy Principle—0
A Compressed Sensing Approach for Distribution Matching—0
AutoBayes: A Compositional Framework for Generalized Variational Inference—0
BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks—0
A Bayesian Tensor Factorization Model via Variational Inference for Link Prediction—0
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection—0
BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models—0
A Mathematical Trust Algebra for International Nation Relations Computation and Evaluation—0
Black-Box Autoregressive Density Estimation for State-Space Models—0
Bayesian Kolmogorov Arnold Networks (Bayesian_KANs): A Probabilistic Approach to Enhance Accuracy and Interpretability—0
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Benchmark Results

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