用神经网络加速贝叶斯混合效应模型推断，快得多且结果可比。
metabeta -- A fast neural model for Bayesian mixed-effects regression
- 用神经网络拟合后验分布，训练后快速推断
- 在真实与模拟数据上达到与MCMC相当的精度
- 适合需要快速贝叶斯推断的科研场景
分层数据中每个组有多个观测值，在经验科学中非常普遍，通常使用混合效应回归分析。此类模型中，贝叶斯推断能提供不确定性估计，但解析不可解，需依赖马尔可夫链蒙特卡洛（MCMC）方法进行昂贵的近似。神经后验估计将大部分计算从推断阶段转移到预训练阶段，通过已知真实值的模拟数据集实现计算摊销。本文提出metabeta，一种用于贝叶斯混合效应回归的神经网络模型。在模拟和真实数据上，其性能稳定且与基于MCMC的参数估计相当，仅需极小时间开销，为贝叶斯混合效应建模开辟了新应用场景。
原文摘要 · Abstract (English)
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.
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