bayesian-modeling

Trigger Boundary

Bayesian and statistical modeling workflows with PyMC, diagnostics, simulation, and model reporting.

Use this aggregate Codex App skill when the task matches one of the source workflows below.

Source Workflows

• bayesian-ppl-diagnostics: Executable guidance for Bayesian workflows using Stan/CmdStanPy, brms, PyMC, and ArviZ, with legacy JAGS/OpenBUGS paths and Rcpp acceleration patterns. Reference: _src/diag/source.md
• pymc: Bayesian modeling with PyMC: model specification, priors, NUTS/VI fitting, diagnostics, posterior predictive checks, LOO/WAIC comparison, prediction, and code review. Reference: _src/pymc/source.md
• statsmodels: Implement and review statsmodels analyses: OLS/GLM/mixed models, time series, robust covariance, diagnostics, coefficient tables, and reproducible inference workflows. Reference: _src/stats/source.md
• statistical-analysis: Plan and report statistical analyses: estimand framing, test/model choice, assumption checks, power/sensitivity notes, effect sizes, uncertainty, and reproducible reporting. Reference: _src/analysis/source.md
• simpy: Build and validate process-based discrete-event simulations in Python with SimPy: entities, resources, queues, event timing, replications, sensitivity analysis, and reporting. Reference: _src/sim/source.md

Workflow

1. Choose the source workflow whose trigger boundary best matches the user request.
2. Read that source workflow's source.md before acting.
3. Load only the needed files under that workflow's copied references, scripts, assets, or evals.
4. Follow the source workflow unless the current project gives stricter instructions.