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 6.x+), including hierarchical models, MCMC sampling (NUTS), variational inference, posterior predictive checks, and model comparison (LOO, WAIC).

Current Version and Setup

PyMC 6.0.1 is the current stable release as of June 2026. It requires Python 3.12+, uses PyTensor 3 as the computational graph backend, and defaults to compiled backends such as Numba. For reproducible local environments, pin the version:

uv pip install "pymc[nutpie]==6.0.1"

The nutpie extra enables the faster Rust/Numba NUTS implementation. If using NumPyro or BlackJAX, install those optional sampler dependencies in the same environment and pin them in the project lockfile.

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

Never sample first and check later. The eight-step workflow — documented with code in references/standard_workflow.md — is:

1. Data preparation — including standardizing predictors so priors are interpretable.
2. Model building — priors and likelihood in a pm.Model context.
3. Prior predictive check — confirm the priors imply plausible data before fitting.
4. Fit model — pm.sample() with an explicit seed.
5. Check diagnostics — R-hat, ESS, divergences. Divergences invalidate the fit; fix the model or reparameterize rather than raising target_accept and hoping.
6. Posterior predictive check — does the fitted model reproduce the observed data?
7. Analyze results — summaries and intervals from the posterior.
8. Make predictions — on new data via pm.set_data and posterior predictive sampling.

Reusable model structures and model comparison are in references/model_patterns.md.

Distribution Selection Guide

For Priors

Scale parameters (σ, τ):

• pm.HalfNormal('sigma', sigma=1) - Default choice
• pm.Exponential('sigma', lam=1) - Alternative
• pm.Gamma('sigma', alpha=2, beta=1) - More informative

Unbounded parameters:

• pm.Normal('theta', mu=0, sigma=1) - For standardized data
• pm.StudentT('theta', nu=3, mu=0, sigma=1) - Robust to outliers

Positive parameters:

• pm.LogNormal('theta', mu=0, sigma=1)
• pm.Gamma('theta', alpha=2, beta=1)

Probabilities:

• pm.Beta('p', alpha=2, beta=2) - Weakly informative
• pm.Uniform('p', lower=0, upper=1) - Non-informative (use sparingly)

Correlation matrices:

• pm.LKJCholeskyCov('chol', n=n_vars, eta=2, sd_dist=pm.HalfNormal.dist(1)) - Preferred covariance prior
• pm.LKJCorr('corr', n=n_vars, eta=2) - Correlation-only prior; eta=1 uniform, eta>1 prefers identity

For Likelihoods

Continuous outcomes:

• pm.Normal('y', mu=mu, sigma=sigma) - Default for continuous data
• pm.StudentT('y', nu=nu, mu=mu, sigma=sigma) - Robust to outliers

Count data:

• pm.Poisson('y', mu=lambda) - Equidispersed counts
• pm.NegativeBinomial('y', mu=mu, alpha=alpha) - Overdispersed counts
• pm.ZeroInflatedPoisson('y', psi=psi, mu=mu) - Excess zeros
• pm.HurdleNegativeBinomial('y', psi=psi, mu=mu, alpha=alpha) - Excess zeros plus overdispersion

Binary outcomes:

• pm.Bernoulli('y', p=p) or pm.Bernoulli('y', logit_p=logit_p)

Categorical outcomes:

• pm.Categorical('y', p=probs)

See: references/distributions.md for comprehensive distribution reference

Sampling and Inference

MCMC with NUTS

Default and recommended for most models:

idata = pm.sample(
    draws=2000,
    tune=1000,
    chains=4,
    target_accept=0.9,
    random_seed=42
)

Adjust when needed:

• Divergences → target_accept=0.95 or higher
• Slow sampling → Use ADVI for initialization
• Discrete parameters → Use pm.Metropolis() for discrete vars

Variational Inference

Fast approximation for exploration or initialization:

with model:
    approx = pm.fit(n=20000, method='advi')

    # Use for initialization
    initvals = approx.sample(return_inferencedata=False)[0]
    idata = pm.sample(initvals=initvals)

Trade-offs:

• Much faster than MCMC
• Approximate (may underestimate uncertainty)
• Good for large models or quick exploration

See: references/sampling_inference.md for detailed sampling guide

Diagnostic Scripts

Comprehensive Diagnostics

from scripts.model_diagnostics import create_diagnostic_report

create_diagnostic_report(
    idata,
    var_names=['alpha', 'beta', 'sigma'],
    output_dir='diagnostics/'
)

Creates:

• Trace plots
• Rank plots (mixing check)
• Autocorrelation plots
• Energy plots
• Local ESS plots
• Summary statistics CSV

Quick Diagnostic Check

from scripts.model_diagnostics import check_diagnostics

results = check_diagnostics(idata)

Checks R-hat, ESS, divergences, and tree depth.

Common Issues and Solutions

Divergences

Symptom: idata.sample_stats.diverging.sum() > 0

Solutions:

1. Increase target_accept=0.95 or 0.99
2. Use non-centered parameterization (hierarchical models)
3. Add stronger priors to constrain parameters
4. Check for model misspecification

Low Effective Sample Size

Symptom: ESS < 400

Solutions:

1. Sample more draws: draws=5000
2. Reparameterize to reduce posterior correlation
3. Use QR decomposition for regression with correlated predictors

High R-hat

Symptom: R-hat > 1.01

Solutions:

1. Run longer chains: tune=2000, draws=5000
2. Check for multimodality
3. Improve initialization with ADVI

Slow Sampling

Solutions:

1. Use ADVI initialization
2. Reduce model complexity
3. Increase parallelization: cores=8, chains=8
4. Use variational inference if appropriate

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)

Workflow

1. Start simple, add complexity gradually
2. Prior predictive check → Fit → Diagnostics → Posterior predictive check
3. Iterate on model specification based on checks
4. Document assumptions and prior choices

Resources

This skill includes:

References (references/)

• 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: Complete workflow examples and code patterns for common model types, data preparation, prior selection, and model validation. Use as a cookbook for standard Bayesian analyses.

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 built on PSIS-LOO ELPD, the only criterion ArviZ 1.x compare() ranks on. 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

with model:
    pm.set_data({'X_data': X_new})
    pred = pm.sample_posterior_predictive(idata, predictions=True)

Additional Notes

• PyMC integrates with ArviZ for visualization and diagnostics; PyMC 6 / ArviZ 1 use xarray DataTree while retaining familiar groups such as .posterior and .posterior_predictive
• 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