4 papers
On WAIC for Dependent Data: A Covariance-Corrected Framework with Linear-Time Complexity
Safaa K. Kadhem
The Widely Applicable Information Criterion (WAIC) is a cornerstone of Bayesian model selection, but its conditional independence assumption renders it inappropriate for sequential…
Regularized Regression by Composition: Identifiability, Structured Penalization, and Statistical Guarantees for Multi-Flow Distributional Models
Safaa K. Kadhem
Regression by composition provides a flexible framework for constructing conditional distributions through sequential group actions. However, when multiple flows act on the same di…
Balancing Efficiency and Feasibility: A Sensitivity Analysis of the Augmentation Parameter in the Finite Selection Model
Safaa K. Kadhem
This paper investigates the role of the augmentation parameter in the Finite Selection Model (FSM) and its impact on estimator performance. Through a comprehensive Monte Carlo simu…
Covariance-Corrected WAIC for Bayesian Sequential Data Models
Safaa K. Kadhem
This paper introduces and develops a theoretical extension of the widely applicable information criterion (WAIC), called the Covariance-Corrected WAIC (CC-WAIC), that applied for B…