Alterlab Pymc
Skill Verified ActivePart 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.
Features
- 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
Use Cases
- Building complex statistical models for research
- Performing uncertainty quantification in predictions
- Comparing and selecting the best Bayesian model
- Diagnosing and troubleshooting MCMC sampling issues
Non-Goals
- Performing classical frequentist statistical analysis
- Data visualization outside of model diagnostics and posterior analysis
- Automated hyperparameter optimization for machine learning models
Installation
npx skills add AlterLab-IEU/AlterLab-Academic-SkillsRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
VerifiedTrust Signals
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