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Predictive Distribution and Calibration Uncertainty

Python-facing port of predictive-distribution-and-calibration-uncertainty.Rmd from the frozen R gp3bayes 0.5.0 reference. The statistical and governance framing below follows the canonical vignette; executable Python workflows use the mapped APIs listed later.

Posterior predictive diagnostics can retain uncertainty in entire outcome distributions as well as in predictive scores.

For binary models:

These summaries do not automatically establish calibration, predictive adequacy, or model superiority.

Python API mapping

  • gp3bayespy.binary_calibration_uncertainty
  • gp3bayespy.binary_calibration_uncertainty_table
  • gp3bayespy.create_predictive_distribution_atlas
  • gp3bayespy.plot_binary_calibration_uncertainty
  • gp3bayespy.plot_prediction_score_uncertainty
  • gp3bayespy.plot_predictive_atlas_statistics
  • gp3bayespy.plot_predictive_quantile_envelope
  • gp3bayespy.prediction_score_uncertainty
  • gp3bayespy.prediction_score_uncertainty_table
  • gp3bayespy.predictive_quantile_envelope

Python usage

import gp3bayespy as gp

# All functions listed above are available from the package root.
# Use help(gp.<function>) or the API reference for the exact Python signature.

An executable workflow for this family is included in ../../examples/predictive_diagnostics.py.