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 2051–2060 of 2226 papers

TitleStatusHype
Distributed Variational Bayesian Algorithms Over Sensor Networks—0
Distributionally Robust Optimisation with Bayesian Ambiguity Sets—0
Distribution learning via neural differential equations: minimal energy regularization and approximation theory—0
Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity—0
Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation—0
Divide, Conquer, Combine Bayesian Decision Tree Sampling—0
Do Bayesian Neural Networks Improve Weapon System Predictive Maintenance?—0
Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluation—0
Domain Agnostic Conditional Invariant Predictions for Domain Generalization—0
Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference—0
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Benchmark Results

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