Hi,

I have been struggling with this for considerable amount of time now and haven’t had much of a success. In short, I’m calibrating the parameters of the bunch of surrogate models (linear or/and RSM).

I often use Uniform distribution for priors but there are cases when I can be more “precise” with prior definition. I’ve tried various approaches and the code below is the closest to what I need except it only calibrates the last parameter (clearly the `for`

loop doesn’t work as intended). Can you please advice on best way to implement this. I’ve tried `np.append`

, `tt.stack`

and `tt.concatenate`

but just can’t get it right.

```
with pm.Model():
for i, pri in enumerate(pri_inputs):
if pri[0] == 1:
priors = pm.Uniform("priors_{}".format(i), lower=pri[1], upper=pri[2])
elif pri[0] == 2:
priors = pm.TruncatedNormal("priors_{}".format(i), mu=pri[1], sigma=pri[2], lower=pri[3], upper=pri[4])
# Define the surrogate models for the outputs using the priors defined above
linear = sm_linear * priors
rsm = tt.sum(sm_rsm * priors * priors.T, axis=2)
out_mu = sm_intercept + tt.sum(linear, axis=0) + tt.sum(rsm, axis=1)
# Define loglikelihood as Multivariate Normal with defined covariance matrix and observations
loglikelihood = pm.MvNormal(
"loglikelihood", mu=out_mu, cov=true_cov, observed=measured
)
# Inference
step = pm.NUTS() # using NUTS sampling
trace = pm.sample(3500, step=step, cores=1, progressbar=True)
```

If I assumed that all the priors are Uniform I can replace the `for`

loop with the code below and the model works perfectly fine. Any suggestions would be much appreciated.

```
priors = pm.Uniform( "priors", pri_inputs[:, 1], pri_inputs[:, 2], shape=len(pri_inputs))[:, None]
```

Regards,

Pawel