Data Science & Analytics
PyMC
Builds, fits, checks, and compares Bayesian models in Python with PyMC and ArviZ. Covers hierarchical (multilevel) models, NUTS MCMC sampling, variational inference (ADVI), prior and posterior predictive checks, convergence diagnostics (R-hat, ESS, divergences), and LOO/WAIC model comparison. Use when writing a PyMC model for regression, count, or binary data. Use when fitting a hierarchical model with partial pooling. Use when diagnosing divergences, low ESS, or high R-hat. Use when comparing candidate models with LOO. Use when choosing priors or running prior predictive checks. Not for non-Bayesian regression; use statsmodels or scikit-learn instead.
- Publisher / source
- KalarisLabs/research-agent-skills (found on the CSV list)
- Upstream record
- KalarisLabs/research-agent-skills
- Version
- Version 2
- Published
- Publication review
- Approved for publication
- Content fingerprint
cfb35701b0a0c7178d6e8c2d86108eb31eb8a2fa7b4f0fc01778dd517c8789c9