AI RESEARCH
Fitting Large Nonlinear Mixed Effects Models Using Variational Expectation Maximization
arXiv CS.LG
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ArXi:2604.26160v1 Announce Type: cross Nonlinear Mixed Effects models (NLME) models are widely used in pharmacometrics and related fields to analyze hierarchical and longitudinal data. However, as the number of parameters and random effects increases, traditional methods for maximizing the marginal likelihood become computationally expensive. This paper explores the Variational Expectation Maximization (VEM) algorithm, a scalable alternative for fitting NLME models. Originally