DiagnosticsPlots.posterior

DiagnosticsPlots.posterior(var_names=None, group='posterior', idata=None, dims=None, figsize=None, backend=None, return_as_pc=False, kind='kde', visuals=None, aes=None, aes_by_visuals=None, **pc_kwargs)

Plot 1-D marginal KDE distributions for one or more posterior variables.

Thin wrapper around azp.plot_dist.

Parameters:

var_nameslist[str] | str | None, optional

Variable(s) to plot. None plots all variables in group.

groupstr, default “posterior”

InferenceData group to draw from. Use "prior" to quickly inspect the prior without calling prior_vs_posterior.

idataaz.InferenceData, optional

Override instance data for this call only.

dimsdict[str, Any], optional

Coordinate filters, e.g. {"channel": ["tv", "radio"]}.

figsizetuple[float, float], optional

Figure size forwarded via figure_kwargs.

backendstr, optional

Rendering backend. Non-matplotlib backends require return_as_pc=True.

return_as_pcbool, default False

If True, return the raw PlotCollection.

kindstr, default “kde”

Plot kind forwarded to azp.plot_dist (e.g. "kde", "hist").

visualsdict, optional

Forwarded to azp.plot_dist.

aesdict, optional

Forwarded to azp.plot_dist as an explicit keyword argument.

aes_by_visualsdict, optional

Forwarded to azp.plot_dist.

**pc_kwargs

Forwarded to azp.plot_dist.

Returns:

tuple[Figure, NDArray[Axes]] or PlotCollection

Examples

fig, axes = mmm.plot.diagnostics.posterior()
fig, axes = mmm.plot.diagnostics.posterior(
    var_names=["alpha"], dims={"channel": ["tv"]}
)