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pymc skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

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Install the pymc skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/pymc ~/.claude/skills/pymc
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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:

the model or reparameterize rather than raising target_accept and hoping.

  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
  1. Posterior predictive check — does the fitted model reproduce the observed data?
  2. Analyze results — summaries and intervals from the posterior.
  3. 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=nvars, eta=2, sddist=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', logitp=logitp)

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

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