pymc
Category: data-science-and-ml · License: Apache License, Version 2.0 · Version: 1.4 · Includes scripts
Tags: Bayesian statistics, MCMC, probabilistic programming, hierarchical models, statistics
When to use it
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.Example requests
Ask your agent in plain language, for example:- “Can you help me fit a hierarchical logistic model for patient response across 12 hospitals? I want partial pooling.”
- “My PyMC model on trial_counts.csv (Poisson likelihood, site random effects) gives ‘There were 214 divergences after tuning’ and r_hat of 1.07 for tau. Raising target_accept to 0.99 didn’t help. What should I do?”
- “I have two candidate models for reaction times in a psychology experiment, one with a per-subject slope and one without. How do I decide which one predicts new subjects better, with uncertainty on the difference, in a Bayesian framework?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole data-science-and-ml category with /plugin install data-science-and-ml@research-agent-skills.