Guide Bayesian modeling with PyMC v5 and diagnose convergence using ArviZ.
npx skills add https://github.com/pymc-labs/agent-skills --skill pymc-modeling
Please help me install this Agent Skill. Skill: pymc-modeling Source: https://github.com/pymc-labs/agent-skills/tree/main/skills/pymc-modeling Command: npx skills add https://github.com/pymc-labs/agent-skills --skill pymc-modeling
This skill consolidates expert guidance for Bayesian modeling with PyMC v5+, enabling AI assistants to provide task-ready workflows, templates, and best practices rather than scattered references.
High-intent search queries and answers about installing and using this skill.
To run Bayesian inference with PyMC, define your model with priors and likelihood, execute sampling using PyMC NUTS or nutpie backends, and inspect inference data with ArviZ diagnostics like r_hat and ess.
Diagnose MCMC convergence in Bayesian modeling by using ArviZ workflows to inspect r_hat, ess, and divergences, ensuring reliable posterior distributions and model reproducibility across your inference data.
Yes, PyMC v5+ supports hierarchical models, Gaussian processes, time series, BART, and mixtures, providing ready-to-use templates and best-practice patterns like non-centered parameterization for complex model families.
Compare Bayesian models using ArviZ by evaluating information criteria like LOO and WAIC, which quantify predictive accuracy and help you select the best model specification for your data.
PyMC supports causal inference patterns including DO/observe workflows, and provides templates for posterior predictive checks to validate your model's fit against observed data.
Besides the default PyMC NUTS backend, you can run MCMC sampling using nutpie or NumPyro/JAX backends to accelerate Bayesian inference workflows depending on your computational environment.
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