Also for this reason PyMC (v4) does not allow you to do prior or posterior predictive with bounded variables. In general you should use Truncated and we will likely deprecate Bound soon.

There is one tiny advantege to Bound in that when you don’t need the extra normalization term you don’t have to compute it, but most users are not aware of when this is the case so we prefer to not mention it in the future.

Note that you can obtain the same effect of Bound by passing an IntervalTransform to continuous distributions

```
from pymc.distributions.transforms import Interval
# These are equivalent
pm.Normal("x", transform=Interval(-1, None)
pm.Bound("y", pm.Normal.dist(), lower=-1)
```