metabeta -- A fast neural model for Bayesian mixed-effects regression
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Abstract (arXiv)
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 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, enabling new use cases for Bayesian mixed-effects modeling.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 36 of its 65 references that are in the map. First posted on arXiv 8 Oct 2025.
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36 of 65 references.
References · 36
- 2017Attention Is All You Needcited by 60,361
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2016Gaussian Error Linear Units (GELUs)cited by 3,502
- 2015Variational Inference with Normalizing Flowscited by 2,170
- 2016Density estimation using Real NVPcited by 2,042
- 2016Variational Inference: A Review for Statisticianscited by 1,472
- 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
- 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 792
- 2019Neural Spline Flowscited by 596
- 2017Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysiscited by 243
- 2019Automatic Posterior Transformation for Likelihood-Free Inferencecited by 219
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- 2017Flexible statistical inference for mechanistic models of neural dynamicscited by 163
- 2024The Road Less Scheduledcited by 163
- 2021Benchmarking Simulation-Based Inferencecited by 162
- 2016Automatic Differentiation Variational Inferencecited by 159
- 2020BayesFlow: Learning complex stochastic models with invertible neural networkscited by 144
- 2018Meta-Learning Probabilistic Inference For Predictioncited by 110
- 2023Flow Matching for Scalable Simulation-Based Inferencecited by 101
- 2024Normalizing Flows are Capable Generative Modelscited by 91
- 2020PMLB v1.0: An open source dataset collection for benchmarking machine learning methodscited by 58
- 2013Hamiltonian Monte Carlo for Hierarchical Modelscited by 52
- 2016Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimationcited by 52
- 2022Robust Neural Posterior Estimation and Statistical Model Criticismcited by 46
- 2023BayesFlow: Amortized Bayesian Workflows With Neural Networkscited by 32
- 2025Can Transformers Learn Full Bayesian Inference in Context?cited by 31
- 2021Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergencecited by 29
- 2024TabPFGen -- Tabular Data Generation with TabPFNcited by 28
- 2021HNPE: Leveraging Global Parameters for Neural Posterior Estimationcited by 19
- 2025Amortized In-Context Bayesian Posterior Estimationcited by 15
- 2024Amortized Bayesian Multilevel Modelscited by 14
- 2025Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptationcited by 13
- 2020Robust model training and generalisation with Studentising flowscited by 10
- 2023Hierarchical Neural Simulation-Based Inference Over Event Ensemblescited by 9
- 2024Conditional diffusions for amortized neural posterior estimationcited by 7
- 2025Multivariate Latent Recalibration for Conditional Normalizing Flowscited by 3
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Citing papers per year: 2026: 2.
2 papers.