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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 591–600 of 2226 papers

TitleStatusHype
Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks—0
Pedestrian Trajectory Forecasting Using Deep Ensembles Under Sensing Uncertainty—0
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo AlgorithmsCode0
Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation—0
On the Convergence of Black-Box Variational Inference—0
Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic LikelihoodCode0
Bayesian calibration of differentiable agent-based models—0
Simultaneous identification of models and parameters of scientific simulatorsCode0
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior InferenceCode0
Adversarial robustness of amortized Bayesian inferenceCode0
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Benchmark Results

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