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pymc

Construct and validate Bayesian hierarchical models with PyMC and ArviZ.

2|Updated Jun 4, 2026
Search Tags:#statistics#inference#mcmc#bayesian#pymc#probabilistic
One-click install
npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill pymc-lord1egypt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pymc
Source: https://github.com/Lord1Egypt/scientific-agent-toolkit/tree/main/scientific-skills/pymc
Command: npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill pymc-lord1egypt

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?

This skill addresses the complexity of building, fitting, and validating probabilistic models, helping users navigate the steep learning curve of Bayesian statistics and MCMC sampling.

Core Features & Use Cases

  • Bayesian Workflow: Provides a structured approach to building hierarchical models, performing MCMC sampling, and conducting prior/posterior predictive checks.
  • Model Comparison: Includes automated utilities for comparing models using LOO and WAIC criteria to ensure robust statistical selection.
  • Diagnostic Reporting: Offers automated diagnostic scripts to detect convergence issues, low effective sample size, and sampling divergences.

Quick Start

Use the pymc skill to build a hierarchical model for my grouped dataset and generate a diagnostic report to verify chain convergence.

Frequently Asked Questions about pymc

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

FAQPage Schema
How do I build and validate a Bayesian hierarchical model using PyMC?▼

Build a Bayesian hierarchical model using PyMC by defining probabilistic variables, performing MCMC sampling, and running prior and posterior predictive checks to validate model fit and convergence.

What is the best way to compare Bayesian models using LOO and WAIC criteria?▼

Compare Bayesian models using LOO and WAIC criteria by leveraging automated utilities within the PyMC and ArviZ workflow to ensure robust statistical selection and validate model performance.

How do I detect MCMC convergence issues and low effective sample size during Bayesian inference?▼

Detect MCMC convergence issues, low effective sample size, and sampling divergences by running automated diagnostic reporting scripts that analyze your Bayesian inference sampling chains.

Does this Bayesian workflow support variational inference alongside MCMC sampling?▼

Yes, the Bayesian workflow supports both variational inference and MCMC sampling, allowing you to choose the most suitable probabilistic inference method for your statistical modeling needs.

Do I need NumPy and ArviZ installed to run probabilistic programming tasks and diagnostic analysis?▼

Yes, you need PyMC, ArviZ, and NumPy installed to perform rigorous statistical inference, probabilistic programming tasks, and comprehensive diagnostic analysis on your models.

Why are my MCMC sampling chains showing divergences during Bayesian model fitting?▼

MCMC sampling divergences during Bayesian model fitting indicate convergence issues or problematic model geometry, which you can identify and diagnose using automated diagnostic reporting scripts.

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