Pymc bayesian linear regression starter

PyMC Bayesian Linear Regression Starter

Use this skill to fit a tiny Bayesian linear regression with PyMC and summarize posterior means plus credible intervals.

What it does

• Loads a deterministic toy (x, y) table.
• Fits a simple Bayesian regression with PyMC.
• Summarizes posterior means, 90% intervals, and a few posterior predictive means.
• Returns compact JSON suitable for downstream scientific inference workflows.

When to use it

• You need a local starter for Bayesian modeling in science workflows.
• You want a minimal example of PyMC sampling and ArviZ-based posterior summarization.

Example

slurm/envs/statistics/bin/python skills/statistical-and-machine-learning-foundations-for-science/pymc-bayesian-linear-regression-starter/scripts/run_pymc_linear_regression.py \
  --input skills/statistical-and-machine-learning-foundations-for-science/pymc-bayesian-linear-regression-starter/examples/toy_observations.tsv \
  --out scratch/pymc/linear_regression_summary.json

Verification

• Skill-local tests: python3 -m unittest discover -s skills/statistical-and-machine-learning-foundations-for-science/pymc-bayesian-linear-regression-starter/tests -p 'test_*.py'
• Repository smoke: python3 -m unittest tests.smoke.test_frontier_domain_skills -v