Hello,

I am trying to visualize simple linear regression with highest posterior density (hpd) for multiple groups. However, I have a problem to apply hpd. Whenever I ran this code, I am extracting the same posterior density for each condition. I would like to visualize posterior density that corresponds to it’s group. How can I plot hpd’s for each group with arviz.plot_hpd?

```
import pymc3 as pm
import numpy as np
import matplotlib.pyplot as plt
import arviz as az
import pandas as pd
# data
data = pd.read_csv('www_MCMC/MCMC/data.csv')
rsp = data['Mean Response'].values
rt = data['Mean Reaction Time'].values
idx = pd.Categorical(data['Structure'], categories=['No Background', 'Only Road', 'Only Dot Ground', 'Dot Terrain + Dot Ground', 'Space', 'Full Background']).codes
groups = len(np.unique(idx))
# model
with pm.Model() as rsp_rt:
α = pm.Normal('α', mu=0, sd=10, shape=groups)
β = pm.Normal('β', mu=0, sd=10, shape=groups)
ϵ = pm.HalfCauchy('ϵ', 10)
μ = pm.Deterministic('μ', α[idx] + β[idx] * rt)
y_pred = pm.Normal('y_pred2', mu=μ, sd=ϵ, observed=rsp)
trace_rsp_rt = pm.sample(cores=1)
# graphics
_, ax_rsp_rt = plt.subplots(2, 3, figsize=(10, 5), sharex=True, sharey=True, constrained_layout=True)
ax_rsp_rt = np.ravel(ax_rsp_rt)
for i in range(groups):
alpha = trace_rsp_rt['α'][:, i].mean()
beta = trace_rsp_rt['β'][:, i].mean()
ax_rsp_rt[i].plot(rt, alpha + beta * rt, c='k', label= f'rsp = {alpha:.2f} + {beta:.2f} * rt')
az.plot_hpd(rt, trace_rsp_rt['μ'], credible_interval=0.98, color='k', ax=ax_rsp_rt[i])
ax_rsp_rt[i].legend()
plt.xlim(1.2, 1.8)
plt.ylim(0.6, 1)
```