pymc-labs/python-analytics-skills

model-evaluation

Load when the user is comparing Bayesian models, computing LOO-CV / ELPD, calling arviz_stats.loo or arviz_stats.compare, doing model stacking/averaging, or computing Bayes factors. Covers the ArviZ 1.1 LOO/ELPD/stacking APIs exclusively (no waic). Triggers…

原文の言語: 英語

prior-elicitation

Load when the user is choosing priors, running prior predictive checks, calling find_constrained_prior, using PreliZ, or otherwise eliciting domain knowledge into a Bayesian model. Covers weakly informative priors, constrained priors, sensitivity analysis,…

原文の言語: 英語

pymc-extras

Load when the user is working with pymc-extras (pmx) features: splines / BSplineBasis, distributional regression / GAMLSS, R2D2M2CP or horseshoe priors, discrete variable marginalization, or Laplace approximation via fit_laplace. Triggers include:…

原文の言語: 英語

pymc-modeling

Load whenever the user is working on code that imports pymc, pytensor, or arviz, or asks about Bayesian modeling, MCMC, priors, posteriors, sampling, or model diagnostics. Covers PyMC 6+, PyTensor 3+, ArviZ 1.1+ (DataTree API), pymc-bart, pymc-extras, nutpie,…

原文の言語: 英語

pymc-testing

Load when writing or modifying pytest tests that touch pymc.Model, pm.sample, or any PyMC model code. Covers pymc.testing.mock_sample, pytest fixtures for Bayesian models, and the distinction between fast structure-only tests (mocking) and slow posterior…

原文の言語: 英語

marimo-notebook

ALWAYS use when: creating/editing marimo notebooks, working with any .py file containing @app.cell decorators, building reactive Python notebooks, doing exploratory data analysis in notebook form, converting Jupyter (.ipynb) to marimo, or when user mentions…

原文の言語: 英語