Pointwise LOO Influence Atlases

Python-facing port of loo-influence-atlas.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.

Aggregate PSIS-LOO summaries can be read together with observation-level predictive contributions and influence diagnostics.

Flagged observations request inspection and are never removed automatically.

Python API mapping

• gp3bayespy.create_loo_influence_atlas
• gp3bayespy.loo_flagged_data
• gp3bayespy.loo_influence_summary
• gp3bayespy.plot_loo_influence_rank
• gp3bayespy.plot_loo_pareto_vs_elpd
• gp3bayespy.plot_loo_pointwise_elpd

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/loo_model_comparison.py.

Visual companion

These figures complement the canonical ported narrative and are generated from package functions.

Pointwise ELPD-LOO. Deterministic visual companion generated from the Python package.

Pareto-k versus ELPD. Deterministic visual companion generated from the Python package.

Ranked PSIS-LOO influence. Deterministic visual companion generated from the Python package.

The figures remain descriptive evidence and do not create automatic inferential decisions.