A Study of Systematic Ratio Changes Based on Bayesian Hierarchical Logistic Regression Modeling

As a robust regression approach in machine learning, the Bayesian hierarchical logistic regression model integrates the strengths of Bayesian statistics and logistic regression, offering high accuracy and interpretability. This paper focuses on the application of this model to investigate dynamic changes in binary classification systems under varying environmental conditions. Initially, a data-driven computational framework was constructed based on age-structured modeling to simulate system dynamics. Next, the Bayesian hierarchical logistic regression model was employed to estimate ratio variations across different system configurations, using watershed-type data as a case study. Finally, after 350 iterations of predictive simulations, the outcomes provided insights into ratio shifts and their cascading effects on associated systems, demonstrating that such variations can lead to significant imbalances in system dynamics.