Posterior KDEs

Posterior KDEs#

KDE plot of the variable mu from the centered eight model. The sample_dims parameter is used to restrict the KDE computation along the draw dimension only.”

Matplotlib version of plot_dist_kde

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from arviz_base import load_arviz_data

import arviz_plots as azp

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

data = load_arviz_data("centered_eight")
pc = azp.plot_dist(
    data,
    kind="kde",
    var_names=["mu"],
    sample_dims=["draw"],    
    backend="none"  # change to preferred backend
)
pc.show()

See also

API Documentation: plot_dist

EABM chapter on Visualization of Random Variables with ArviZ

Other examples with plot_dist#

Faceted ECDF plots for 1D marginals of the distribution

Posterior ECDFs

Posterior ECDFs

Faceted histogram plots for 1D marginals of the distribution. The point_estimate_text option is set to False to omit that visual from the plot.

Posterior Histograms

Posterior Histograms

Full marginal distribution comparison between different models

Posterior KDEs for two models

Posterior KDEs for two models

Draw lines on plots to highlight specific thresholds, targets, or important values.

Add Lines

Add Lines

Download Python Source Code: plot_dist_kde.py

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