4 papers · 1 filter
Variational Bayesian Sparse Negative Binomial Regression
Mitra Kharabati, Morteza Amini, Mohammad Arashi
Count data with overdispersion and high-dimensional predictors pose significant challenges in modern applications. While negative binomial regression offers a flexible modeling fra…
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…
Variational Inference for Sparse Poisson Regression
Mitra Kharabati, Morteza Amini, Mohammad Arashi
We have utilized the non-conjugate Variational Bayesian (VB) method for the problem of the sparse Poisson regression model. To provide approximate conjugacy in the model, the likel…