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 1801–1810 of 2226 papers

TitleStatusHype
A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems—0
Solving Bongard Problems with a Visual Language and Pragmatic Reasoning—0
Mean Field Network based Graph Refinement with application to Airway Tree Extraction—0
Model selection and parameter inference in phylogenetics using Nested SamplingCode0
A Compressed Sensing Approach for Distribution Matching—0
Stochastic Gradient Hamiltonian Monte Carlo with Variance Reduction for Bayesian Inference—0
Pseudo-marginal Bayesian inference for supervised Gaussian process latent variable models—0
Accurate Evaluation of Asset Pricing Under Uncertainty and Ambiguity of Information—0
From Shannon's Channel to Semantic Channel via New Bayes' Formulas for Machine Learning—0
Locally Private Bayesian Inference for Count ModelsCode0
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Benchmark Results

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