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Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 301–310 of 2226 papers

TitleStatusHype
A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach—0
Accelerating a hybrid continuum-atomistic fluidic model with on-the-fly machine learning—0
A deep learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generation—0
A Novel Gaussian Process Based Ground Segmentation Algorithm with Local-Smoothness Estimation—0
Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data—0
Bayesian Differential Privacy through Posterior Sampling—0
A Deep Learning Approach to Dst Index Prediction—0
A normative theory of social conflict—0
A Bayesian/Information Theoretic Model of Bias Learning—0
A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics—0
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Benchmark Results

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