Individual skill
pymc-bayesian-modeling
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
backend/cli/skills/coding/pymc/SKILL.md
Source commit a08e0ad0ad3d6448123039ee4659163eb2e35d99
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 2, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 4,352
- Repository last updated
- Nov 6, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent Repository topic
- ai Repository topic
- bun Repository topic
- cli Repository topic
- llm Repository topic
- ml Repository topic
- open-source Repository topic
- research Repository topic
- science Repository topic
- ai-agent Repository topic
- bayesian Declared skill tag
- mcmc Declared skill tag
- probabilistic-programming Declared skill tag
- statistics Declared skill tag
- data Derived category
- engineering Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesSKILL.mdcore · 16,226 bytesassets/hierarchical_model_template.pyruns · 11,858 bytesassets/linear_regression_template.pyruns · 8,118 bytesreferences/distributions.mddoc · 10,834 bytesreferences/sampling_inference.mddoc · 10,384 bytesreferences/workflows.mddoc · 14,106 bytesscripts/model_comparison.pyruns · 12,411 bytesscripts/model_diagnostics.pyruns · 9,600 bytes
Read excerpt
# 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)