Complex Distribution

Complex distribution modeling focuses on accurately representing and learning probability distributions that deviate significantly from simple, standard forms, often exhibiting multimodality, long tails, or high dimensionality. Current research emphasizes developing flexible models, such as normalizing flows, Gaussian mixture models, and transformer-based architectures, often combined with techniques like quantile regression or piecewise approaches to handle diverse data characteristics. These advancements are crucial for improving the accuracy of predictions in various fields, including customer lifetime value prediction, electricity consumption forecasting, and short-video recommendation systems, as well as enabling more robust data synthesis and analysis in data-scarce scenarios. The ability to effectively model complex distributions enhances the reliability and interpretability of data-driven insights across numerous scientific and practical applications.