Guidelines for visual predictive checks following Säilynoja et al. recommendations using ArviZ
Generate visual predictive checks for Bayesian models using ArviZ to compare simulated vs observed data across continuous, count, binary, and survival outcomes.
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Use this skill when running prior or posterior predictive checks to validate Bayesian models. These checks compare simulated data from the model to observed data (or plausible ranges for prior predictive checks).
generated quantities block (e.g., y_rep for posterior predictive, y_prior_pred for prior predictive)arviz_base.from_cmdstanpy, specifying predictive groupsarviz_plots functionsplot_ppc_dist with kind="ecdf" and kind="kde"plot_ppc_pit - shows calibration with simultaneous bandsplot_ppc_pit(coverage=True) - equal-tailed interval coverageplot_ppc_tstat for median, MAD, IQR combined with combine_plotsplot_loo_pit - avoids double-dipping by using leave-one-outplot_ppc_rootogram - emphasizes discreteness and dispersionplot_ppc_dist(kind="hist")plot_ppc_pava - PAV-adjusted calibration curvesplot_ppc_interval - posterior predictive intervals with observed overlayplot_ppc_censored - Kaplan-Meier style PPCUse multiple complementary views rather than relying on a single plot. For example, for continuous outcomes, combine ECDF (shows full distribution) with PIT ECDF (shows calibration) and t-stat PPCs (shows specific features like central tendency and spread).
LOO-PIT is preferred over regular PIT for posterior checks as it approximates leave-one-out predictive distribution and avoids overfitting concerns.
Name plots descriptively: prior_predictive_ecdf.png, loo_pit_calibration.png, posterior_rootogram.png.
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