Multilevel Model

Multilevel models (MLMs) are statistical frameworks designed to analyze data with hierarchical structures, enabling the joint modeling of relationships across different levels (e.g., individuals within groups, or sensors within a network). Current research emphasizes efficient MLM estimation, particularly using Bayesian approaches and techniques like amortized inference and multilevel Markov Chain Monte Carlo (MCMC) accelerated by machine learning models, to address computational challenges posed by large datasets. These advancements are impacting diverse fields, including federated learning (improving model robustness and addressing class imbalance), risk assessment (analyzing unstructured data), and engineering applications (e.g., source location and predictive maintenance), by providing more accurate and scalable analyses of complex systems.