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stat.ME2025
NExON-Bayes: A Bayesian approach to network estimation informed by ordinal covariates
Joseph Feest, Hélène Ruffieux, Camilla Lingjærde +1
In heterogeneous disease settings, accounting for intrinsic sample variability is crucial for obtaining reliable and interpretable omic network estimates. However, most graphical m…
stat.ME2023
A modelling framework for detecting and leveraging node-level information in Bayesian network inference
Xiaoyue Xi, Hélène Ruffieux
Bayesian graphical models are powerful tools to infer complex relationships in high dimension, yet are often fraught with computational and statistical challenges. If exploited in…