# Alterlab Pymc

> Bayesian modeling and probabilistic programming with PyMC — hierarchical models, MCMC (NUTS) sampling, variational inference, LOO/WAIC model comparison, and posterior predictive checks. Use when fitting Bayesian or hierarchical models, estimating posteriors and credible intervals, running probabilistic inference, or comparing models with LOO/WAIC. Part of the AlterLab Academic Skills suite.

- Skill: `alterlab-ieu/alterlab-pymc` (Agent Skill, multi-file: 13 files)
- Install (CLI): `npx skillmds@latest add alterlab-ieu/alterlab-pymc`
- Raw SKILL.md: https://api.skillmd.com/api/skills/alterlab-ieu/alterlab-pymc/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: Apache-2.0
- Author: AlterLab-IEU (https://skillmd.com/u/alterlab-ieu)
- Updated: 2026-08-19
- Page: https://skillmd.com/skills/alterlab-ieu/alterlab-pymc

---


# PyMC Bayesian Modeling

## Overview

PyMC is a Python library for Bayesian modeling and probabilistic programming. Build, fit, validate, and compare Bayesian models using PyMC's modern API (version 5.x+), including hierarchical models, MCMC sampling (NUTS), variational inference, and model comparison (LOO, WAIC).

## When to Use This Skill

This skill should be used when:
- Building Bayesian models (linear/logistic regression, hierarchical models, time series, etc.)
- Performing MCMC sampling or variational inference
- Conducting prior/posterior predictive checks
- Diagnosing sampling issues (divergences, convergence, ESS)
- Comparing multiple models using information criteria (LOO, WAIC)
- Implementing uncertainty quantification through Bayesian methods
- Working with hierarchical/multilevel data structures
- Handling missing data or measurement error in a principled way

## Standard Bayesian Workflow

Follow this 8-step workflow for building and validating Bayesian models:

1. **Data preparation** — standardize predictors, handle missing data, set up `coords`
2. **Model building** — weakly informative priors, named `dims`, `pm.Data()` for predictables
3. **Prior predictive check** — `pm.sample_prior_predictive`; validate priors *before* fitting
4. **Fit** — `pm.sample(draws=2000, tune=1000, chains=4, target_accept=0.9)`; include `log_likelihood=True` for comparison
5. **Diagnostics** — R-hat < 1.01, ESS > 400, no divergences, good trace mixing
6. **Posterior predictive check** — `pm.sample_posterior_predictive`; check fit vs. observed data
7. **Analyze** — `az.summary`, `az.plot_posterior`, `az.plot_forest`
8. **Predict** — `pm.set_data` then `pm.sample_posterior_predictive`; extract HDI intervals

Full step-by-step code: `references/workflow_examples.md`.

## Common Model Patterns

PyMC supports linear/logistic/Poisson regression, hierarchical (multilevel) models, and
time-series (AR). Ready-to-adapt code for each lives in `references/model_patterns.md`.

**Critical:** Always use non-centered parameterization for hierarchical models to avoid divergences.
Templates: `assets/linear_regression_template.py`, `assets/hierarchical_model_template.py`.

## Distribution Selection

Choosing priors and likelihoods is the highest-leverage modeling decision. A quick chooser
(scale params, unbounded, positive, probabilities, correlation matrices; continuous/count/binary/
categorical likelihoods) is in `references/distribution_selection.md`. The comprehensive catalog
is in `references/distributions.md`.

## Model Comparison and Sampling

- **Compare models** with LOO/WAIC (`scripts/model_comparison.py`); interpret Δloo and Pareto-k.
- **Sample** with NUTS by default; raise `target_accept` for divergences; ADVI for fast approximation.
- **Diagnose** with `scripts/model_diagnostics.py` (`check_diagnostics`, `create_diagnostic_report`).
- **Troubleshoot** divergences, low ESS, high R-hat, slow sampling.

Code and decision rules: `references/model_comparison.md`. Detailed sampling-algorithm guide:
`references/sampling_inference.md`.

## Best Practices

### Model Building

1. **Always standardize predictors** for better sampling
2. **Use weakly informative priors** (not flat)
3. **Use named dimensions** (`dims`) for clarity
4. **Non-centered parameterization** for hierarchical models
5. **Check prior predictive** before fitting

### Sampling

1. **Run multiple chains** (at least 4) for convergence
2. **Use `target_accept=0.9`** as baseline (higher if needed)
3. **Include `log_likelihood=True`** for model comparison
4. **Set random seed** for reproducibility

### Validation

1. **Check diagnostics** before interpretation (R-hat, ESS, divergences)
2. **Posterior predictive check** for model validation
3. **Compare multiple models** when appropriate
4. **Report uncertainty** (HDI intervals, not just point estimates)

Start simple and add complexity gradually, iterating on the model based on each
predictive check (see the 8-step workflow above).

## Resources

This skill includes:

### References (`references/`)

- **`workflow_examples.md`**: Full step-by-step code for the 8-step Bayesian workflow (data prep → predictions).
- **`model_patterns.md`**: Ready-to-adapt code for linear/logistic/Poisson regression, hierarchical, and AR time-series models.
- **`distribution_selection.md`**: Quick chooser for priors and likelihoods by parameter/outcome type.
- **`model_comparison.md`**: LOO/WAIC comparison, diagnostic scripts, and troubleshooting (divergences, ESS, R-hat, slow sampling).
- **`distributions.md`**: Comprehensive catalog of PyMC distributions organized by category (continuous, discrete, multivariate, mixture, time series). Use when selecting priors or likelihoods.
- **`sampling_inference.md`**: Detailed guide to sampling algorithms (NUTS, Metropolis, SMC), variational inference (ADVI, SVGD), and handling sampling issues. Use when encountering convergence problems or choosing inference methods.
- **`workflows.md`**: A single end-to-end runnable script (data prep → save) plus extra cookbook recipes not in `workflow_examples.md` — missing-data imputation, QR reparameterization, mixture models, and a model-averaging helper.

### Scripts (`scripts/`)

- **`model_diagnostics.py`**: Automated diagnostic checking and report generation. Functions: `check_diagnostics()` for quick checks, `create_diagnostic_report()` for comprehensive analysis with plots.

- **`model_comparison.py`**: Model comparison utilities using LOO/WAIC. Functions: `compare_models()`, `check_loo_reliability()`, `model_averaging()`.

### Templates (`assets/`)

- **`linear_regression_template.py`**: Complete template for Bayesian linear regression with full workflow (data prep, prior checks, fitting, diagnostics, predictions).

- **`hierarchical_model_template.py`**: Complete template for hierarchical/multilevel models with non-centered parameterization and group-level analysis.

## Quick Reference

### Model Building
```python
with pm.Model(coords={'var': names}) as model:
    # Priors
    param = pm.Normal('param', mu=0, sigma=1, dims='var')
    # Likelihood
    y = pm.Normal('y', mu=..., sigma=..., observed=data)
```

### Sampling
```python
idata = pm.sample(draws=2000, tune=1000, chains=4, target_accept=0.9)
```

### Diagnostics
```python
from scripts.model_diagnostics import check_diagnostics
check_diagnostics(idata)
```

### Model Comparison
```python
from scripts.model_comparison import compare_models
compare_models({'m1': idata1, 'm2': idata2}, ic='loo')
```

### Predictions
```python
# X must have been wrapped at build time: pm.Data('X', X, dims=('obs', 'predictors'))
with model:
    pm.set_data({'X': X_new}, coords={'obs': range(len(X_new))})
    pm.sample_posterior_predictive(idata, predictions=True, extend_inferencedata=True)
# predictions land in idata.predictions
```

## Additional Notes

- PyMC integrates with ArviZ for visualization and diagnostics
- Use `pm.model_to_graphviz(model)` to visualize model structure
- Save results with `idata.to_netcdf('results.nc')`; load with `az.from_netcdf('results.nc')`
- For very large models, consider minibatch ADVI or data subsampling

**Common gotchas (PyMC 5.x / ArviZ):**
- To predict on new data, the predictors must be wrapped in `pm.Data('X', X, dims=...)` at build time — only then can `pm.set_data({'X': X_new}, coords={...})` swap them. A plain NumPy array baked into the graph cannot be replaced.
- `pm.sample_prior_predictive` takes `draws=` (the old `samples=` keyword was removed).
- For out-of-sample predictions call `pm.sample_posterior_predictive(idata, predictions=True, extend_inferencedata=True, ...)`; results then live in `idata.predictions`, not `idata.posterior_predictive`.