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Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks
Samaneh Nazari, Mohammad Arashi, Abdolnasser Sadeghkhani
Structure learning of directed acyclic graphs (DAGs) from observational data is a foundational task in causal discovery and is widely used to infer regulatory networks from medical…
Bayesian DAG Structure Learning with Simultaneous Shrinkage Covariance Estimation under Scale-Mixture Error Distributions in the Proportional High-Dimensional Regime
Samaneh Nazari, Mohammad Arashi, Abdolnasser Sadeghkhani
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 t…
Multivariate Interval-Valued Models in Frequentist and Bayesian Schemes
Ali Sadeghkhani, Abdolnasser Sadeghkhani
In recent years, addressing the challenges posed by massive datasets has led researchers to explore aggregated data, particularly leveraging interval-valued data, akin to tradition…
Bayesian predictive densities as an interpretation of a class of Skew--Student distributions with application to medical data
Abdolnasser Sadeghkhani
This paper describes a new Bayesian interpretation of a class of skew--Student distributions. We consider a hierarchical normal model with unknown covariance matrix and show th…