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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 1721–1730 of 2226 papers

TitleStatusHype
Adaptive Gaussian process surrogates for Bayesian inference—0
Bayesian inference for PCA and MUSIC algorithms with unknown number of sourcesCode0
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach—0
Flexible Mixture Modeling on Constrained Spaces—0
Stochasticity from function -- why the Bayesian brain may need no noise—0
Simulator Calibration under Covariate Shift with Kernels—0
Estimating Bayesian Optimal Treatment Regimes for Dichotomous Outcomes using Observational Data—0
Predictive Collective Variable Discovery with Deep Bayesian ModelsCode0
Robustness Guarantees for Bayesian Inference with Gaussian ProcessesCode0
A Variational Observation Model of 3D Object for Probabilistic Semantic SLAM—0
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Benchmark Results

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