metabeta - A fast neural model for Bayesian mixed-effects regression

• URL: http://arxiv.org/abs/2510.07473v1
• Date: Wed, 08 Oct 2025 19:20:00 GMT
• Title: metabeta - A fast neural model for Bayesian mixed-effects regression
• Authors: Alex Kipnis, Marcel Binz, Eric Schulz,
• Abstract summary: We propose metabeta, a transformer-based neural network model for mixed-effects regression.<n>We show that it reaches stable and comparable performance to MCMC-based parameter estimation at a fraction of the usually required time.
• Score: 22.95834831696185
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Hierarchical data with multiple observations per group is ubiquitous in empirical sciences and is often analyzed using mixed-effects regression. In such models, Bayesian inference gives an estimate of uncertainty but is analytically intractable and requires costly approximation using Markov Chain Monte Carlo (MCMC) methods. Neural posterior estimation shifts the bulk of computation from inference time to pre-training time, amortizing over simulated datasets with known ground truth targets. We propose metabeta, a transformer-based neural network model for Bayesian mixed-effects regression. Using simulated and real data, we show that it reaches stable and comparable performance to MCMC-based parameter estimation at a fraction of the usually required time.

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