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 the current API (PyMC ≥ 6 with ArviZ ≥ 1.1), including hierarchical models, MCMC sampling (NUTS), variational inference, and PSIS-LOO model comparison.

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uv pip install "pymc[nutpie]" "arviz[matplotlib,h5netcdf]"   # nutpie sampler, plotting backend, NetCDF I/O

PyMC 6 changed several defaults and names that most tutorials still use — read Version notes at the end before reusing PyMC 5 / ArviZ 0.x code.

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 with PSIS-LOO cross-validation
• Implementing uncertainty quantification through Bayesian methods
• Working with hierarchical/multilevel data structures
• Handling missing data or measurement error in a principled way

Does NOT Trigger

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), then pm.compute_log_likelihood(idata) if you will compare models
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_dist (was 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.

Use the non-centered parameterization for hierarchical models when groups have few observations: the centered form creates funnel geometry that NUTS explores poorly, which shows up as 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 PSIS-LOO (scripts/model_comparison.py); interpret elpd_diff against dse and check Pareto-k. ArviZ 1.x has no WAIC.
• Sample with NUTS by default (nutpie when installed); 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. Call pm.compute_log_likelihood(idata) before 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: PSIS-LOO 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 (handles both PyMC-NUTS and nutpie sample stats). Functions: check_diagnostics() for quick checks, create_diagnostic_report() for comprehensive analysis with plots.

• model_comparison.py: PSIS-LOO model comparison utilities. 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

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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

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idata = pm.sample(draws=2000, tune=1000, chains=4, target_accept=0.9)  # returns xarray.DataTree
pm.compute_log_likelihood(idata)  # needed for LOO / az.compare

Diagnostics

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from scripts.model_diagnostics import check_diagnostics
check_diagnostics(idata)

Model Comparison

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from scripts.model_comparison import compare_models
compare_models({'m1': idata1, 'm2': idata2})  # PSIS-LOO; higher elpd is better

Predictions

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# 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 (needs the graphviz package)
• Save results with idata.to_netcdf('results.nc') (requires h5netcdf or netCDF4, e.g. arviz[h5netcdf]); load with az.from_netcdf('results.nc')
• For very large models, consider minibatch ADVI or data subsampling

Version notes (PyMC 6 / ArviZ 1.x)

PyMC 6.0 (May 2026) moved to ArviZ 1.x and PyTensor 3. Code written for PyMC 5 / ArviZ 0.x fails in the ways below.

• Results are an xarray.DataTree, not az.InferenceData. Group access (idata.posterior, idata["posterior"]) still works; idata.extend(other) became idata.update(other); list groups with list(idata.children).
• Log-likelihood: call pm.compute_log_likelihood(idata) after sampling. pm.sample(idata_kwargs={"log_likelihood": True}) is deprecated.
• Sampler: nutpie is the default NUTS sampler when installed (400 tuning steps by default; PyMC's own NUTS keeps 1000). Select explicitly with nuts_sampler="pymc" | "nutpie" | "numpyro" | "blackjax". Sample-stat names differ (tree_depth vs depth), so diagnostics code should check which exists. Starting values go in initvals= (start= fails).
• Backend: PyTensor 3 compiles with Numba by default, so the first call includes JIT-compilation time.
• Summaries: az.summary reports 89% equal-tailed intervals (eti89_lb, eti89_ub) instead of 94% HDI (hdi_3%, hdi_97%). Use az.summary(idata, ci_kind="hdi", ci_prob=0.94) or set az.rcParams["stats.ci_kind"] / ["stats.ci_prob"]. az.hdi(..., prob=0.95) replaces hdi_prob=.
• Plots return a PlotCollection (save with pc.savefig("file.png"), show with pc.show()); ax= / axes= arguments are gone. Renames: plot_posterior → plot_dist, plot_ppc → plot_ppc_dist (prior checks: group="prior_predictive", num_samples= instead of num_pp_samples=), plot_trace → plot_trace_dist (trace plus density), plot_dist_comparison → plot_prior_posterior. Install a backend with arviz[matplotlib].
• Model comparison: az.waic is gone and az.compare(dict) takes no ic=/scale=; it ranks by PSIS-LOO elpd (higher is better) with stacking weights and reports elpd_diff, dse, p_worse, and diag_* columns.
• 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.