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pymc-fundamentals

Educates users on writing and diagnosing PyMC 5 Bayesian models with ArviZ.

9|1|Updated Jan 10, 2026
Search Tags:#diagnostics#sampling#inference#bayesian#arviz#pymc#probabilistic-programming
One-click install
npx skills add https://github.com/choxos/BiostatAgent --skill pymc-fundamentals
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pymc-fundamentals
Source: https://github.com/choxos/BiostatAgent/tree/main/plugins/bayesian-modeling/skills/pymc-fundamentals
Command: npx skills add https://github.com/choxos/BiostatAgent --skill pymc-fundamentals

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides foundational knowledge for writing and reviewing PyMC 5 models, guiding users through syntax, priors, likelihoods, sampling, and ArviZ diagnostics to ensure correct Bayesian workflows.

Core Features & Use Cases

  • Model construction: Define priors, likelihoods, and deterministic relationships in PyMC 5.
  • Sampling & diagnostics: Run MCMC sampling and interpret ArviZ outputs to assess convergence and fit.
  • Model conversion: Translate models from Stan/JAGS to PyMC and compare results.

Quick Start

Create a simple PyMC 5 model with a Normal prior and run a short MCMC sample to obtain the posterior trace.

Frequently Asked Questions about pymc-fundamentals

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

FAQPage Schema
How do I build a Bayesian model with priors and likelihoods in PyMC 5?▼

Yes, you can translate models from Stan or JAGS to PyMC. The skill guides users through converting existing Stan and JAGS workflows to PyMC 5 syntax, enabling you to compare posterior results across frameworks.

How do I run MCMC sampling and use ArviZ for diagnostics in PyMC?▼

Run MCMC sampling in PyMC and interpret ArviZ outputs to assess convergence and model fit. The skill covers sampling strategies and robust diagnostic workflows to evaluate posterior traces accurately.

What is non-centered parameterization and when do I need it in Bayesian inference?▼

No prior PyMC experience is required to learn Bayesian modeling fundamentals. The skill provides foundational knowledge on syntax, priors, likelihoods, and sampling, making it suitable for data scientists and researchers building their first models.

Can I convert existing Stan or JAGS models to PyMC 5?▼

Yes, you can translate models from Stan or JAGS to PyMC. The skill guides users through converting existing Stan and JAGS workflows to PyMC 5 syntax, enabling you to compare posterior results across frameworks.

Why does my PyMC MCMC sampling fail to converge?▼

MCMC sampling convergence issues are diagnosed using ArviZ outputs. The skill teaches how to interpret ArviZ diagnostics, apply non-centered parameterizations, and select appropriate sampling strategies to achieve robust convergence.

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