Fitting Large Nonlinear Mixed Effects Models Using Variational Expectation Maximization
Nonlinear Mixed Effects (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 introduced in the context of probabilistic graphical models and later popularized through variational autoencoders, VEM has not been extensively applied to NLME modeling. By leveraging flexible variational families and reverse-mode automatic differentiation, VE...
| Authors | Mohamed Tarek, Pedro Afonso |
|---|---|
| Primary category | stat.ME |
| Published | 28 April 2026 |
| Last updated at source | 22 September 2026 |
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