pymc
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
npx skills add K-Dense-AI/scientific-agent-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 6.x+), including hierarchical models, MCMC sampling (NUTS), variational inference, posterior predictive checks, and model comparison (LOO, WAIC). ## Current Version and Setup PyMC 6.0.1 is the current stable release as of June 2026. It requires Python 3.12+, uses PyTensor 3 as the computational graph backend, and defaults to compiled backends such as Numba. For reproducible local environments, pin the version: ```bash uv pip install "pymc[nutpie]==6.0.1" ``` The `nutpie` extra enables the faster Rust/Numba NUTS implementation. If using NumPyro or BlackJAX, install those optional sampler dependencies in the same environment and pin them in the project lockfile. ## 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 multipl
- Overview
- Current Version and Setup
- When to Use This Skill
- Standard Bayesian Workflow
- Distribution Selection Guide
- For Priors
- For Likelihoods
- Sampling and Inference
- MCMC with NUTS
- Variational Inference
- Diagnostic Scripts
- Comprehensive Diagnostics
- Quick Diagnostic Check
- Common Issues and Solutions
uv pip install "pymc[nutpie]==6.0.1"
What does the pymc 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 K-Dense-AI/scientific-agent-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 K-Dense-AI/scientific-agent-skills, a repository with 32,619 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.