Alterlab Pymc
技能 已验证 活跃Part of the AlterLab Academic Skills suite. Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
To provide a robust and well-guided experience for users wanting to perform Bayesian statistical modeling and probabilistic programming with PyMC.
功能
- Build hierarchical Bayesian models
- Perform MCMC sampling (NUTS)
- Utilize variational inference
- Compare models with LOO/WAIC
- Conduct posterior predictive checks
- Generate predictions with uncertainty quantification
使用场景
- Building complex statistical models for research
- Performing uncertainty quantification in predictions
- Comparing and selecting the best Bayesian model
- Diagnosing and troubleshooting MCMC sampling issues
非目标
- Performing classical frequentist statistical analysis
- Data visualization outside of model diagnostics and posterior analysis
- Automated hyperparameter optimization for machine learning models
安装
npx skills add AlterLab-IEU/AlterLab-Academic-Skills通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
已验证类似扩展
PyMC Bayesian Modeling
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Alterlab Statistical Analysis
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SHAP Model Interpretability
100Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.