AI 中文总结
该研究评估了PyMC、NumPyro、TensorFlow Probability的变分推断默认设置，发现其即便对简单模型也会产生有偏近似，各软件实现存在差异，依赖默认设置可能导致隐性故障或差的近似结果。
AI 中文摘要
我们从标准从业者的角度系统评估了一套现成的变分推断（VI）软件包，利用简单的解析基准模型评估PyMC、NumPyro和TensorFlow Probability中默认VI设置的准确性与稳定性。与以往聚焦方法学进展的研究不同，我们的评估重点是普通用户会接触到的软件实现和默认配置。结果显示，即便对于简单的一维共轭模型，默认设置也可能产生后验摘要的有偏近似；各软件在初始化和变换的控制上存在差异，依赖默认设置可能导致隐性故障或糟糕的近似结果。
英文摘要
We systematically evaluate a suite of off-the-shelf variational inference (VI) software packages from the perspective of a standard practitioner. Using simple analytic benchmark models, we assess the accuracy and stability of the default VI settings in PyMC, NumPyro, and TensorFlow Probability. Unlike previous research focusing on methodological advances, our evaluation emphasizes software implementation and the default configurations that typical users encounter. Our results show that default settings can yield biased approximations of posterior summaries even for simple one-dimensional conjugate models, controls of initialization and transformations differ between software implementations, and relying on defaults may yield silent failures or poor approximations.
