Hi,

I’m trying to model the data with two AR1 models.

The code below is a modification of the AR1 example from the official pymc3 documentation. I create a `group_idx`

(with 0, 1) to separate the data into two groups. But I keep getting `ValueError: Input dimension mis-match. (input[0].shape[0] = 5000, input[1].shape[0] = 4999)`

. I guess the AR model behaves differently from distributions.

What is the right way to achieve this?

```
import pymc3 as pm
import arviz as az
import numpy as np
RANDOM_SEED = 8927
np.random.seed(RANDOM_SEED)
T = 10000
y = np.zeros((T,))
# true stationarity:
true_theta = 0.95
# true standard deviation of the innovation:
true_sigma = 2.0
# true process mean:
true_center = 0.0
for t in range(1, T):
y[t] = true_theta * y[t - 1] + np.random.normal(loc=true_center, scale=true_sigma)
y = y[-5000:]
group_idx = np.zeros_like(y, dtype=int)
group_idx[1000:] = 1
with pm.Model() as ar1:
# assumes 95% of prob mass is between -2 and 2
theta = pm.Normal("theta", 0.0, 1.0, shape=2)
# precision of the innovation term
tau = pm.Exponential("tau", 0.5, shape=2)
# process mean
center = pm.Normal("center", mu=0.0, sigma=1.0)
likelihood = pm.AR1("y", k=theta[group_idx], tau_e=tau[group_idx], observed=y - center)
trace = pm.sample(1000, tune=2000, init="advi+adapt_diag", random_seed=RANDOM_SEED)
idata = az.from_pymc3(trace)
```