Hi all,

I’m trying to translate a code in PyMC3 into PyMC5 but the code below doesn’t work.

So if anyone know what to do, let me know.

My PyMC version is 5.16.2 and python version is 3.11.6

```
import numpy as np
import pymc as pm
import matplotlib.pyplot as plt
import arviz as az
n = 30
x = np.zeros(n)
y = np.zeros(n, dtype=int)
Dist_s = [2.4, 2.8]
Dist_w = [0.8, 1.6]
rng = np.random.default_rng(10)
for i in range(n):
wk = rng.random()
y[i] = 0 * (wk < 0.5) + 1 * (wk >= 0.5)
x[i] = rng.random() * Dist_w[y[i]] + Dist_s[y[i]]
data = {'x': x, 'y': y}
# centerize the data
data['x_c'] = data['x'] - data['x'].mean()
with pm.Model() as model_l:
beta0 = pm.Normal('beta0', mu=0, sigma=100)
beta1 = pm.Normal('beta1', mu=0, sigma=100)
mu = pm.Deterministic('mu', pm.math.sigmoid(beta0 + beta1 * data['x_c']))
boundary = pm.Deterministic('boundary', -beta0 / beta1)
pm.Bernoulli('y', p=mu, observed=data['y'])
trace_l = pm.sample(random_seed=1)
trace_l_ext = az.extract(trace_l)
fig, ax = plt.subplots(constrained_layout=True)
ax.scatter(data['x'], data['y'], color=[f'C{i}' for i in data['y']])
mu = trace_l_ext['mu'].mean(axis=0)
idx = np.argsort(data['x'])
ax.plot(data['x'][idx], mu[idx])
# pm.plot_hdi(data['x'], trace_l['mu'], ax=ax) # for pymc3
pm.plot_hdi(data['x'], trace_l_ext['mu'], ax=ax) # doesn't work
# pm.plot_hdi(data['x'], pp.posterior_predictive['mu'], ax=ax) # doesn't work
plt.vlines(trace_l_ext['boundary'].mean(axis=0) + data['x'].mean(), 0, 1, color='k')
boundary_hdi = pm.hdi(trace_l_ext['boundary']) + data['x'].mean()
ax.fill_betweenx([0, 1], boundary_hdi[0], boundary_hdi[1], alpha=0.5)
ax.set_xlabel(r'$x$')
ax.set_ylabel(r'$y$')
```

The problem is `pm.plot_hdi()`

.

In version 3, `pm.plot_hdi(data['x'], trace_l['mu'], ax=ax)`

worked, but I don’t know what to input to the 2nd argument in version 5.

Could anyone help me?