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 861–870 of 2226 papers

TitleStatusHype
Bayesian Inference for the Multinomial Probit Model under Gaussian Prior Distribution—0
Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems—0
Efficient Bayesian species tree inference under the multi-species coalescent—0
Efficient Bayesian synthetic likelihood with whitening transformations—0
Efficient Debiased Evidence Estimation by Multilevel Monte Carlo Sampling—0
Bayesian Inference in Cumulative Distribution Fields—0
Bayesian inference in hierarchical models by combining independent posteriors—0
Efficient Likelihood Bayesian Constrained Local Model—0
Efficient Low-Order Approximation of First-Passage Time Distributions—0
Evidence of Replica Symmetry Breaking under the Nishimori conditions in epidemic inference on graphs—0
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Benchmark Results

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