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 941–950 of 2226 papers

TitleStatusHype
Exploring Action-Centric Representations Through the Lens of Rate-Distortion Theory—0
Bayesian Inference for Neighborhood Filters With Application in Denoising—0
Bayesian Inference for Multidimensional Welfare Comparisons—0
An uncertainty-aware Digital Shadow for underground multimodal CO2 storage monitoring—0
Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference—0
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data—0
Extending the statistical software package Engine for Likelihood-Free Inference—0
Extension of compressive sampling to binary vector recovery for model-based defect imaging—0
Factorized Asymptotic Bayesian Inference for Factorial Hidden Markov Models—0
DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural Anomaly Detection—0
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Benchmark Results

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