# pymc-bayesian-modeling

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

- Source: https://doutorai.pt/en/components/skills__pymc-bayesian-modeling
- Type: Skills
- Category: Scientific
- Downloads: 0
- Site: Doutor AI (https://doutorai.pt/en) - AI tools for small businesses

An open-source community component, curated and adapted by Doutor AI.

## What it does and how to use it

A **skill** that teaches Claude Code to do scientific tasks well. It is packaged knowledge the model uses whenever it is relevant.

1. Install the skill (command above).
2. In Claude Code, describe what you need - the skill turns on by itself when the context fits.
3. No commands to memorize: you talk in plain language and the skill does the rest.

## Install (Claude Code)

```bash
mkdir -p .claude/skills/pymc-bayesian-modeling && curl -fsSL https://doutorai.pt/data/lib/skills__pymc-bayesian-modeling.md -o .claude/skills/pymc-bayesian-modeling/SKILL.md
```

Paste this command into the terminal, in your project folder, and the component installs automatically.

## Content

```
---
name: pymc-bayesian-modeling
description: "Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference."
---

# 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 5.x+), including hierarchical models, MCMC sampling (NUTS), variational inference, and model comparison (LOO, WAIC).

## When to Use This Skill

This skill should be used 

- 🔒 Full content on the Pro plan (€7.99/month) -
```

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Content by Doutor AI (https://doutorai.pt/en). When quoting, please cite the source: https://doutorai.pt/en/components/skills__pymc-bayesian-modeling