Pareto shape parameter diagnostics

Pareto shape parameter diagnostics#

Visualize Pareto k diagnostics from PSIS-LOO-CV to assess the reliability of importance sampling for each observation.

The Pareto k diagnostic indicates how reliable the importance sampling approximation is for each observation. Values below 0.7 are generally considered good, while higher values suggest the importance weights are unreliable and the LOO estimates may be inaccurate for those observations.

This plot helps identify problematic observations that may be influential causing the importance sampling to be unreliable.

Matplotlib version of plot_khat

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from arviz_base import load_arviz_data
from arviz_stats import loo

import arviz_plots as azp

azp.style.use("arviz-variat")

dt = load_arviz_data("radon")
elpd_data = loo(dt, pointwise=True)

pc = azp.plot_khat(
    elpd_data,
    threshold=0.7,
    show_hlines=True,
    show_bins=True,
    backend="none",  # change to preferred backend
)

pc.show()

See also

API Documentation: plot_khat

Other examples with plot_khat#

Download Python Source Code: plot_khat.py

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