Author

Samaneh Nazari

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Preprint Jul 2026

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks

A Laplace-approximated Bayesian scoring function for the non-conjugate Normal--Gamma prior is developed and, through the DAG-probit extension, predicts malignancy from nuclear morphometry with a cross-validated ROC-AUC of $0.94$ using a sparse, interpretable set of direct predictors.

Samaneh Nazari, Mohammad Arashi, Abdolnasser Sadeghkhani · 0 citations

Preprint Jul 2026

Bayesian DAG Structure Learning with Simultaneous Shrinkage Covariance Estimation under Scale-Mixture Error Distributions in the Proportional High-Dimensional Regime

We propose a unified Bayesian framework namely robust DAG-Cholesky horseshoe (R-DACH) for joint directed acyclic graph (DAG) structure learning and precision matrix estimation in the high-dimensional proportional asymptotic regime $p/n \to c \in (0,\infty)$, under the scale mixture of normal errors. The construction pl...

Samaneh Nazari, Mohammad Arashi, Abdolnasser Sadeghkhani · 0 citations