DiagnosticsPlots.prior_predictive

DiagnosticsPlots.prior_predictive(original_scale=True, hdi_prob=0.94, idata=None, dims=None, figsize=None, backend=None, return_as_pc=False, line_kwargs=None, hdi_kwargs=None, observed_kwargs=None, **pc_kwargs)

Plot time series from the prior predictive distribution.

Mirrors posterior_predictive but draws from the prior_predictive group. Each panel overlays the prior mean line, an HDI band, and the observed target for comparison.

Parameters:

original_scalebool, default True

If True, plots y_original_scale from idata.prior (where PyMC stores Deterministics) and the observed target in original units. If False, plots y from idata.prior_predictive (where PyMC stores observed variables) and the observed target in scaled units.

hdi_probfloat, default 0.94

Probability mass of the HDI band.

idataaz.InferenceData, optional

Override instance data.

dimsdict[str, Any], optional

Subset dimensions.

figsizetuple[float, float], optional

backendstr, optional

return_as_pcbool, default False

line_kwargsdict, optional

Forwarded to azp.visuals.line_xy for the predictive mean line.

hdi_kwargsdict, optional

Forwarded to azp.visuals.fill_between_y.

observed_kwargsdict, optional

Forwarded to azp.visuals.line_xy for the observed data line.

**pc_kwargs

Forwarded to PlotCollection.wrap().

Returns:

tuple[Figure, NDArray[Axes]] or PlotCollection

Examples

fig, axes = mmm.plot.diagnostics.prior_predictive()
fig, axes = mmm.plot.diagnostics.prior_predictive(original_scale=False)