Skill · em Dados, IA e pesquisa

pymc-bayesian-modeling

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

Procedência

Origem: davila7/claude-code-templates
Caminho: cli-tool/components/skills/scientific/pymc
Versão fixada: 57f899e5394bb8ca166f38eacae8f0853cbfe033
Licença: MIT
Espelhado em 25/09/2026
nenhum download no Claude Code Templates (lido em 25/09/2026)

Antes de instalar

8 arquivos · 92,3 KB · inclui 4 scripts que executam: assets/hierarchical_model_template.py, assets/linear_regression_template.py, scripts/model_comparison.py, scripts/model_diagnostics.py

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/skills/pymc-bayesian-modeling/.

d=".claude/skills/pymc-bayesian-modeling"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/pymc"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/assets/hierarchical_model_template.py" "$u/assets/hierarchical_model_template.py" \
  -o "$d/assets/linear_regression_template.py" "$u/assets/linear_regression_template.py" \
  -o "$d/references/distributions.md" "$u/references/distributions.md" \
  -o "$d/references/sampling_inference.md" "$u/references/sampling_inference.md" \
  -o "$d/references/workflows.md" "$u/references/workflows.md" \
  -o "$d/scripts/model_comparison.py" "$u/scripts/model_comparison.py" \
  -o "$d/scripts/model_diagnostics.py" "$u/scripts/model_diagnostics.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.claude/skills/pymc-bayesian-modeling/.

d="$HOME/.claude/skills/pymc-bayesian-modeling"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/pymc"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/assets/hierarchical_model_template.py" "$u/assets/hierarchical_model_template.py" \
  -o "$d/assets/linear_regression_template.py" "$u/assets/linear_regression_template.py" \
  -o "$d/references/distributions.md" "$u/references/distributions.md" \
  -o "$d/references/sampling_inference.md" "$u/references/sampling_inference.md" \
  -o "$d/references/workflows.md" "$u/references/workflows.md" \
  -o "$d/scripts/model_comparison.py" "$u/scripts/model_comparison.py" \
  -o "$d/scripts/model_diagnostics.py" "$u/scripts/model_diagnostics.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Codex

Neste projeto: instala em .agents/skills/pymc-bayesian-modeling/.

d=".agents/skills/pymc-bayesian-modeling"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/pymc"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/assets/hierarchical_model_template.py" "$u/assets/hierarchical_model_template.py" \
  -o "$d/assets/linear_regression_template.py" "$u/assets/linear_regression_template.py" \
  -o "$d/references/distributions.md" "$u/references/distributions.md" \
  -o "$d/references/sampling_inference.md" "$u/references/sampling_inference.md" \
  -o "$d/references/workflows.md" "$u/references/workflows.md" \
  -o "$d/scripts/model_comparison.py" "$u/scripts/model_comparison.py" \
  -o "$d/scripts/model_diagnostics.py" "$u/scripts/model_diagnostics.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.agents/skills/pymc-bayesian-modeling/.

d="$HOME/.agents/skills/pymc-bayesian-modeling"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/pymc"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/assets/hierarchical_model_template.py" "$u/assets/hierarchical_model_template.py" \
  -o "$d/assets/linear_regression_template.py" "$u/assets/linear_regression_template.py" \
  -o "$d/references/distributions.md" "$u/references/distributions.md" \
  -o "$d/references/sampling_inference.md" "$u/references/sampling_inference.md" \
  -o "$d/references/workflows.md" "$u/references/workflows.md" \
  -o "$d/scripts/model_comparison.py" "$u/scripts/model_comparison.py" \
  -o "$d/scripts/model_diagnostics.py" "$u/scripts/model_diagnostics.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Antigravity

Neste projeto: instala em .agents/skills/pymc-bayesian-modeling/.

d=".agents/skills/pymc-bayesian-modeling"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/pymc"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/assets/hierarchical_model_template.py" "$u/assets/hierarchical_model_template.py" \
  -o "$d/assets/linear_regression_template.py" "$u/assets/linear_regression_template.py" \
  -o "$d/references/distributions.md" "$u/references/distributions.md" \
  -o "$d/references/sampling_inference.md" "$u/references/sampling_inference.md" \
  -o "$d/references/workflows.md" "$u/references/workflows.md" \
  -o "$d/scripts/model_comparison.py" "$u/scripts/model_comparison.py" \
  -o "$d/scripts/model_diagnostics.py" "$u/scripts/model_diagnostics.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.gemini/antigravity-cli/skills/pymc-bayesian-modeling/.

d="$HOME/.gemini/antigravity-cli/skills/pymc-bayesian-modeling"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/pymc"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/assets/hierarchical_model_template.py" "$u/assets/hierarchical_model_template.py" \
  -o "$d/assets/linear_regression_template.py" "$u/assets/linear_regression_template.py" \
  -o "$d/references/distributions.md" "$u/references/distributions.md" \
  -o "$d/references/sampling_inference.md" "$u/references/sampling_inference.md" \
  -o "$d/references/workflows.md" "$u/references/workflows.md" \
  -o "$d/scripts/model_comparison.py" "$u/scripts/model_comparison.py" \
  -o "$d/scripts/model_diagnostics.py" "$u/scripts/model_diagnostics.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Peça ao Rook

Já usa o Rook Labs? Cole no chat do Rook: instale a skill https://rooklabs.sh/marketplace/cct.pymc-bayesian-modeling

Prévia do SKILL.md

---
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 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

Follow this workflow for building and validating Bayesian models:

### 1. Data Preparation

```python
import pymc as pm
import arviz as az
import numpy as np

# Load and prepare data
X = ...  # Predictors
y = ...  # Outcomes

# Standardize predictors for better sampling
X_mean = X.mean(axis=0)
…

Ver todo o marketplace