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Declared Priors versus Fitted Posteriors

Python-facing port of declared-prior-versus-posterior.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.

The bridge uses the backend-independent prior specification retained by gp3bayespy; saved backend prior draws are not required.

Shift, contraction, interval overlap, Kolmogorov-Smirnov distance, and quantile-based Wasserstein distance are descriptive marginal summaries, not automatic measures of prior adequacy.

Python API mapping

  • gp3bayespy.plot_prior_posterior_contraction
  • gp3bayespy.plot_prior_posterior_density
  • gp3bayespy.plot_prior_posterior_intervals
  • gp3bayespy.plot_prior_posterior_shift
  • gp3bayespy.prior_posterior_bridge
  • gp3bayespy.prior_posterior_distance_table
  • gp3bayespy.prior_posterior_summary_table

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.