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 2041–2050 of 2226 papers

TitleStatusHype
Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions—0
Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas Industry—0
Dimension reduction via score ratio matching—0
Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle—0
Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation—0
Discriminative Relational Topic Models—0
DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting—0
Distilling Calibration via Conformalized Credal Inference—0
Distributed Bayesian inference for consistent labeling of tracked objects in non-overlapping camera networks—0
Distributed Bayesian Inference for Large-Scale IoT Systems—0
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Benchmark Results

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