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PyMC Bayesian Modeling

Guides Claude through the complete PyMC 5 Bayesian workflow — priors, NUTS sampling, convergence diagnostics, LOO model comparison, and prediction.

Data & AnalyticsAdvanced★ 367⑂ 36AI score 9/10Last updated: Aug 29, 2026

What it does

Turns Claude into a disciplined Bayesian modeling assistant built around PyMC 5.

  • 8-step workflow: standardize data → define model and priors → prior predictive check → NUTS sampling → convergence diagnostics → posterior predictive check → LOO/WAIC comparison → prediction
  • Diagnostic thresholds: R-hat < 1.01, ESS > 400, zero divergences, Pareto-k < 0.7, with prescribed fixes for each failure
  • Prior/distribution selection table: Normal, HalfNormal, StudentT, Beta, Dirichlet, NegativeBinomial and when each applies
  • Ready recipes: hierarchical models with non-centered parameterization, logistic regression, Gaussian Processes
  • Troubleshooting table: divergent transitions, low ESS, slow sampling, logp = -inf, max tree depth

Who it's for

  • Researchers and analysts who need full uncertainty quantification (HDIs), not just point estimates
  • Anyone modeling grouped or nested data (patients within hospitals, students within schools)
  • Biostatistics, clinical trial, psychometrics, and Bayesian A/B testing practitioners
  • PyMC users who get stuck interpreting diagnostics or comparing candidate models

Examples

  1. "Fit a hierarchical model of recovery time across 20 hospitals" → produces non-centered parameterization code plus an az.summary walkthrough
  2. "My sampler reported 150 divergences" → raises target_accept to 0.95, suggests reparameterization and tighter priors, then re-checks R-hat/ESS
  3. "Which is better, the linear or quadratic model?" → refits with log_likelihood=True, runs az.compare(ic="loo") and validates with Pareto-k diagnostics

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/jaechang-hits/SciAgent-Skills/HEAD/skills/biostatistics/pymc-bayesian-modeling/SKILL.md

What I want: (describe your task here)

If Claude can't open the link, open it yourself and paste the contents instead.

↓ If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the skills/biostatistics/pymc-bayesian-modeling folder from the GitHub repo jaechang-hits/SciAgent-Skills into my ~/.claude/skills/pymc-bayesian-modeling/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/jaechang-hits/SciAgent-Skills.git && mkdir -p ~/.claude/skills && cp -r SciAgent-Skills/skills/biostatistics/pymc-bayesian-modeling ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open your terminal.
  2. Clone the repository: git clone https://github.com/jaechang-hits/SciAgent-Skills.git
  3. Create the skills directory: mkdir -p ~/.claude/skills
  4. Copy this skill: cp -r SciAgent-Skills/skills/biostatistics/pymc-bayesian-modeling ~/.claude/skills/
  5. Install the Python dependencies: pip install "pymc>=5" arviz numpy matplotlib
  6. Restart Claude Code and run /skills to confirm pymc-bayesian-modeling is listed.
  7. Try a prompt such as "Build a Bayesian logistic regression in PyMC for this dataset and check convergence."
View source on GitHub ↗License: NOASSERTION