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 751–760 of 2226 papers

TitleStatusHype
Bayesian calibration of differentiable agent-based models—0
Simultaneous identification of models and parameters of scientific simulatorsCode0
Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic LikelihoodCode0
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior InferenceCode0
Federated Variational Inference: Towards Improved Personalization and Generalization—0
Explaining Emergent In-Context Learning as Kernel Regression—0
PDE-constrained Gaussian process surrogate modeling with uncertain data locationsCode0
Massively Parallel Reweighted Wake-Sleep—0
Augmented Message Passing Stein Variational Gradient Descent—0
A Simple Generative Model of Logical Reasoning and Statistical Learning—0
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Benchmark Results

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