pymc-modeling

Modern Bayesian modeling with PyMC v5+. Key defaults: nutpie sampler (2-5x faster), non-centered parameterization for hierarchical models, HSGP over exact GPs, coords/dims for readable InferenceData, and save-early workflow to prevent data loss from late crashes.

Modeling strategy: Build models iteratively — start simple, check prior predictions, fit and diagnose, check posterior predictions, expand one piece at a time. See references/workflow.md for the full workflow.

Notebook preference: Use marimo for interactive modeling unless the project already uses Jupyter.

使用 PyMC v5+ 进行贝叶斯统计建模。在构建概率模型、指定先验、运行 MCMC 推理、诊断收敛或比较模型时使用。涵盖 PyMC、ArviZ、pymc-bart、pymc-extras、nutpie 和 JAX/NumPyro 后端。触发器涉及的任务包括：贝叶斯推理、后验采样、分层/多级模型、GLM、时间序列、高斯过程、BART、混合模型、先验/后验预测检查、MCMC 诊断、LOO-CV、WAIC、模型比较或使用 do/observe 的因果推理。 来源：pymc-labs/python-analytics-skills。