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Copula-based Models for Synthetic Population Generation with Model Transferability

Core Concepts
Utilizing copula theory enhances model transferability in synthetic population generation.
The content introduces a framework combining copula theory with machine learning generative models for synthetic population generation. It focuses on model transferability, allowing insights from data-rich environments to be applied to contexts with limited data availability. The method involves normalizing the source population using empirical cumulative distribution functions (ECDFs) and leveraging generative models to learn the copula structure for accurate synthesis of populations. Introduction to Population Synthesis and Model Transferability Framework Overview: Copula Theory and Machine Learning Generative Models Methodology: Normalization, Copula Learning, and Synthetic Population Generation Data Sources: American Community Survey (ACS) and Geographical Levels Analysis Evaluation Metrics: SRMSE and Sampled Zeros for Model Performance Assessment Numerical Experiments: State Level, Spatial Transferability at Same Geographical Level, Spatial Transferability at Smaller Geographical Level Conclusion on the Effectiveness of Copula Normalization in Enhancing Model Transferability
"Utilizing copula theory enhances model transferability in synthetic population generation."

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