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 1531–1540 of 2226 papers

TitleStatusHype
Accurate, reliable and interpretable solubility prediction of druglike molecules with attention pooling and Bayesian learning—0
A Channel-based Exact Inference Algorithm for Bayesian Networks—0
A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One—0
A closed-form approach to Bayesian inference in tree-structured graphical models—0
A Closer Look at Disentangling in β-VAE—0
A closer look at parameter identifiability, model selection and handling of censored data with Bayesian Inference in mathematical models of tumour growth—0
A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data—0
A comparison of Bayesian sampling algorithms for high-dimensional particle physics and cosmology applications—0
A compartmental model for Xylella fastidiosa diseases with explicit vector seasonal dynamics—0
A Compressed Sensing Approach for Distribution Matching—0
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Benchmark Results

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