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

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

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

Diagnostics

from scripts.model_diagnostics import check_diagnostics
check_diagnostics(idata)

Model Comparison

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

Predictions

# 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.