Researchers have explored the Variational Expectation Maximization (VEM) algorithm as a scalable method for fitting Nonlinear Mixed Effects (NLME) models, particularly when dealing with a large number of parameters. This approach, drawing from probabilistic graphical models and variational autoencoders, offers an efficient alternative to traditional computationally expensive methods. The paper details VEM's application to NLME, demonstrating its ability to handle models with over 15,000 population parameters using the Pumas statistical software. AI
IMPACT Presents a scalable statistical method that could improve the efficiency of complex model fitting in various scientific domains.
RANK_REASON This is a research paper detailing a new methodology for fitting statistical models.
- DeepNLME Friberg model
- Nonlinear Mixed Effects models
- pharmacometrics
- Variational Expectation Maximization
- warfarin model
- probabilistic graphical models
- variational autoencoders
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