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VEM algorithm scales to fit large nonlinear mixed effects models with over 15,000 parameters

By PulseAugur Editorial · [2 sources] ·

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.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

VEM algorithm scales to fit large nonlinear mixed effects models with over 15,000 parameters

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Mohamed Tarek, Pedro Afonso ·

    Fitting Large Nonlinear Mixed Effects Models Using Variational Expectation Maximization

    arXiv:2604.26160v1 Announce Type: cross Abstract: 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 metho…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Fitting Large Nonlinear Mixed Effects Models Using Variational Expectation Maximization

    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 c…

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