I have made a model with the following observed value, `obs`

:

```
pm.TruncatedNormal("obs", mu=multiplier.T * pred + means_vector, sd=err_sd, observed=self.df['mean_log_gfp_live'].to_numpy(),
lower=0.0, upper=12.0)
```

I then sampled from the model using `sample _prior_predictive()`

.

I was quite surprised that many of the sampled values are apparently `np.inf`

or `-np.inf`

. Shouldn’t this be impossible because of the truncation? Or at best, shouldn’t this give a runtime error?

When I checked, 1977 of my 5000 samples have at least one of their values (`observed`

is a vector of 1043 elements) either `inf`

or `-inf`

.

Probably this means there’s something wrong with my model, but this also suggests that this variable can behave oddly in prior sampling.