Add example to az.plot_hdi that shows how to plot hdi from InferenceData posterior or posterior predictive

Tell us about it

The az.plot_hdi docs only show how to plot the hdi from synthetic data. What would be nice is if it also shows users how to plot the hdi from an az.InferenceData as thats the standard data representation most people will be working with.

Thoughts on implementation

Use one of the precomputed datasets that has a posterior predictive group and create a plot.

Here’s an example of loading a dataset with posterior predictive group https://arviz-devs.github.io/arviz/api/generated/arviz.plot_ppc.html

Here’s an (unrelated) example where PyMC3 generates a trace and posterior predictive group and its used with az.plot_hdi

with pm.Model() as model_linear:
    β = pm.Normal('β', sd=10, shape=2)

    μ = pm.Deterministic('μ', pm.math.dot(babies[["Intercept", "Month"]], β))
    ϵ = pm.HalfNormal('ϵ', sd=10)

    length = pm.Normal('length', mu=μ, sd=ϵ, observed=babies.Length)

    linear_trace = pm.sample(2000, tune=4000)
    linear_ppc = pm.sample_posterior_predictive(inf_data_linear)
    inf_data_linear = az.from_pymc3(trace=linear_trace, posterior_predictive= linear_ppc)

fig, ax = plt.subplots()

ax.set_ylabel("Length")
ax.set_xlabel("Month");

μ_m = inf_data_linear.posterior["μ"].values.reshape(-1, babies.Length.shape[0]).mean(axis=0)

ax.plot(babies.Month, μ_m, c='C4')
az.plot_hdi(babies.Month, inf_data_linear.posterior_predictive["length"], hdi_prob=.94, ax=ax)

ax.plot(babies.Month, babies.Length, 'C0.', alpha=0.1)

plt.savefig('img/Baby_Length_Linear_Fit.png', dpi=300)

[kenkirito]

Using precomputed data set

[OriolAbril]

yes, assigning it to you