Synthetic Population

Synthetic population generation involves creating realistic, artificial populations for various applications, primarily aiming to replicate real-world population statistics and behaviors while addressing data scarcity and privacy concerns. Current research focuses on leveraging machine learning models, including generative adversarial networks (GANs), variational autoencoders (VAEs), and recurrent neural networks (RNNs), often combined with Bayesian methods and optimization techniques, to synthesize diverse and accurate populations with detailed attributes. These synthetic populations are valuable tools for diverse fields, enabling researchers to conduct large-scale simulations for epidemiological modeling, risk assessment, social science experiments, and the development of unbiased machine learning models, among other applications.