Evaluating the Use of Bayesian Networks in Modeling Probabilistic Relationships Among Complex Data Variables

Posted: Jul 31, 1998

• Harper Torres
• Zoey Martin
• Mia Flores

Abstract

Bayesian Networks have long been recognized as powerful tools for representing and reasoning about uncertainty in complex systems. These probabilistic graphical models provide a structured approach to capturing dependencies among variables through directed acyclic graphs and conditional probability distributions. The fundamental appeal of Bayesian Networks lies in their ability to combine rigorous probabilistic reasoning with intuitive graphical representations, enabling both quantitative predictions and qualitative insights into variable relationships. However, as data complexity continues to escalate in modern applications—characterized by high dimensionality, heterogeneous data types, temporal dynamics, and hierarchical structures—traditional Bayesian Network approaches face significant challenges in scalability, learning efficiency, and representational adequacy. This research addresses the critical gap between classical Bayesian Network methodologies and the demands of contemporary complex data environments. We propose that the true potential of Bayesian Networks in complex data modeling remains largely untapped due to methodological limitations in handling the intricate interplay of variables across different scales and domains. Our investigation centers on developing and evaluating enhanced Bayesian Network frameworks that can effectively navigate the complexities of modern datasets while preserving the interpretability that makes Bayesian Networks particularly valuable in high-stakes decision-making contexts.