pymc-bayesian-modeling
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill pymc --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Overview
- When to Use This Skill
- Standard Bayesian Workflow
- 1. Data Preparation
- 2. Model Building
- 3. Prior Predictive Check
- 4. Fit Model
- 5. Check Diagnostics
- 6. Posterior Predictive Check
- 7. Analyze Results
- 8. Make Predictions
- Common Model Patterns
- Linear Regression
- Logistic Regression
What does the pymc-bayesian-modeling skill do?
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill pymc --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.