alterlab-pymc

Execute end-to-end Bayesian modeling workflows in PyMC.

58|9|Updated Mar 16, 2026

Search Tags:#diagnostics #hierarchical #bayesian #arviz #pymc #posterior-predictive

Ready to run

npx skills add https://github.com/AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-pymc

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: alterlab-pymc
Source: https://github.com/AlterLab-IEU/AlterLab-Academic-Skills/tree/main/skills/data-science/alterlab-pymc
Command: npx skills add https://github.com/AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-pymc

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pymc, arviz, numpy, pandas, matplotlib, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Bayesian modeling workflows for researchers require structured guidelines, templates, and diagnostics to build, fit, and validate probabilistic models with PyMC.

Core Features & Use Cases

End-to-end Bayesian workflow: from data preparation and model construction to sampling, diagnostics, and posterior predictive checks.
Templates & references: ready-to-use patterns for linear, hierarchical, and time-series models, plus reference materials.
Educational examples: practical use cases and tutorials for posterior analysis, model comparison, and predictions.
Use Case: a researcher compares multiple models (linear vs hierarchical) on a dataset and selects the best via LOO/WAIC.

Quick Start

Load your dataset and run a PyMC Bayesian model to obtain posterior estimates.

Frequently Asked Questions about alterlab-pymc

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build and sample hierarchical Bayesian models in PyMC?▼

To build and sample hierarchical Bayesian models in PyMC, you prepare your dataset, construct the model structure, execute sampling, and run diagnostics using ArviZ to validate posterior estimates and posterior predictive checks.

What is the standard workflow for end-to-end Bayesian modeling with PyMC?▼

The standard Bayesian modeling workflow in PyMC covers data preparation, model construction for linear or hierarchical examples, sampling, convergence diagnostics, posterior predictive checks, and final predictions.

Can I compare multiple Bayesian models using LOO and WAIC with ArviZ?▼

Yes, you can compare multiple Bayesian models using LOO and WAIC. The workflow provides educational examples and reference patterns for model comparison, allowing you to evaluate linear versus hierarchical models and select the best fit.

Does this Bayesian workflow support posterior predictive checks and predictions?▼

Yes, this Bayesian workflow supports posterior predictive checks and predictions. It includes templates and practical use cases to validate probabilistic models and generate predictions from the posterior distribution using PyMC and ArviZ.

What dependencies do I need for Bayesian modeling and diagnostics with PyMC?▼

For Bayesian modeling and diagnostics with PyMC, you need ArviZ for posterior analysis, NumPy and Pandas for data preparation, and Matplotlib for visualizing posterior predictive checks and model diagnostics.

When should I use hierarchical models instead of linear models in PyMC?▼

You should use hierarchical models instead of linear models in PyMC when your data has nested or grouped structures. The workflow includes ready-to-use templates for both linear and hierarchical models to guide your model construction.