BibTeX

@article{2504.11279v1,
Author        = {Henrik Häggström and Sebastian Persson and Marija Cvijovic and Umberto Picchini},
Title         = {Simulation-based inference for stochastic nonlinear mixed-effects models
  with applications in systems biology},
Eprint        = {2504.11279v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.CO},
Abstract      = {The analysis of data from multiple experiments, such as observations of
several individuals, is commonly approached using mixed-effects models, which
account for variation between individuals through hierarchical representations.
This makes mixed-effects models widely applied in fields such as biology,
pharmacokinetics, and sociology. In this work, we propose a novel methodology
for scalable Bayesian inference in hierarchical mixed-effects models. Our
framework first constructs amortized approximations of the likelihood and the
posterior distribution, which are then rapidly refined for each individual
dataset, to ultimately approximate the parameters posterior across many
individuals. The framework is easily trainable, as it uses mixtures of experts
but without neural networks, leading to parsimonious yet expressive surrogate
models of the likelihood and the posterior. We demonstrate the effectiveness of
our methodology using challenging stochastic models, such as mixed-effects
stochastic differential equations emerging in systems biology-driven problems.
However, the approach is broadly applicable and can accommodate both stochastic
and deterministic models. We show that our approach can seamlessly handle
inference for many parameters. Additionally, we applied our method to a
real-data case study of mRNA transfection. When compared to exact
pseudomarginal Bayesian inference, our approach proved to be both fast and
competitive in terms of statistical accuracy.},
Year          = {2025},
Month         = {Apr},
Url           = {http://arxiv.org/abs/2504.11279v1},
File          = {2504.11279v1.pdf}
}