Henrik Häggström
@henhagg.bsky.social
PhD student @deptmathgothenburg.bsky.social | https://research.chalmers.se/en/person/henhagg
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
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
April 28, 2025 at 1:04 PM
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
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