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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 571–580 of 2226 papers

TitleStatusHype
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware PriorsCode0
Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappingsCode0
In-context Exploration-Exploitation for Reinforcement Learning—0
Scalable Bayesian inference for the generalized linear mixed model—0
A prediction rigidity formalism for low-cost uncertainties in trained neural networks—0
Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming—0
Statistical Mechanics of Dynamical System Identification—0
Stochastic Approximation with Biased MCMC for Expectation MaximizationCode0
Quasi-Bayesian Estimation and Inference with Control Functions—0
Sequential transport maps using SoS density estimation and α-divergencesCode0
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Benchmark Results

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