New paper on SBI for stochastic nonlinear mixed-effects models!
We propose a scalable Bayesian framework for hierarchical mixed-effects models, using amortized likelihood and posterior approximations, obtained without neural networks.
🔗 arxiv.org/abs/2504.11279
