Hi all,

I’m working on a problem where I’m trying to create ordinal regression models to predict ratings in multiple different contexts. However, I have reason to believe that these various contexts are all related, and so I’ve been trying to create a hierarchical model. The code I’ve created thus far is,

```
n_contexts = len(np.unique(context_codes))
with pm.Model() as model:
# for weights
mu_w = pm.Normal("mu_w", mu=0, sd=1)
sigma_w = pm.HalfCauchy("sigma_w", beta=1)
# for thresholds
mu_thresh = pm.distributions.MvNormal(
"mu_thresh",
mu=np.array([-2, -1, 0, 1]),
cov=np.eye(4),
shape=4
)
sigma_thresh = pm.HalfCauchy("sigma_thresh", beta=2)
# weight distribution
w = pm.Normal("w", mu=mu_w, sd=sigma_w, shape=n_contexts)
# thresholds distribution
thresholds = []
for i in range(n_contexts):
thresholds.append(
pm.Normal(
"thresholds_{}".format(i),
mu=mu_thresh,
sd=sigma_thresh,
shape=4,
transform=pm.distributions.transforms.ordered
)
)
wx = w[context_codes] * x
y_ = pm.OrderedLogistic(
"y",
cutpoints=tt.stack(thresholds)[context_codes],
eta=wx,
observed=y
)
```

When I try to create this model though, I get the error, `ValueError: all the input array dimensions except for the concatenation axis must match exactly`

.

The model works when I assume fixed thresholds for all contexts, so I suspect that this has something to do with the way the cutpoints are being set, but I don’t know exactly what’s causing the error. Any help would be really appreciated.