Sensitivity posterior quantities

The posterior quantities are computed by power-scaling the prior or likelihood and visualizing the resulting changes. Sensitivity can then be quantified by considering how much the perturbed quantities differ from the base quantities.

using ArviZPythonPlots, ArviZExampleData, InferenceObjects

use_style("arviz-variat")

idata = load_example_data("rugby")
# the power-scaling sensitivity diagnostic needs every log_likelihood variable to share the
# sample dimensions, so drop the home_team/away_team string covariates that live there too
log_likelihood = idata.log_likelihood[(:home_points, :away_points)]
idata = merge(idata, InferenceData(; log_likelihood))

pc = plot_psense_quantities(
    idata; var_names=["sd_att", "sd_def"], quantities=["mean", "sd", "0.25", "0.75"]
)

See plot_psense_quantities.