Dearl all,

I have implemented a shifted Lognormal

```
class ShiftedLognormal(pymc3.Lognormal):
def __init__(self, mu, sigma, shift, tau=None, sd=None, *args, **kwargs):
transform = pymc3.distributions.transforms.lowerbound(shift)
super().__init__(mu=mu, sd=sigma, tau=tau, *args, **kwargs,
transform=transform)
self.shift = shift
self.mean += shift
self.mode += shift
def random(self):
return super().random() + self.shift
def logp(self, x):
return super().logp(x - self.shift)
```

According to a suggestion found here: https://github.com/pymc-devs/pymc3/issues/864

It seems to work well. However, if I want to sample_posterior_predictive, I get the error:

random() got an unexpected keyword argument ‘point’

I also tried using a non-shifted Lognormal and adding a shift variable to the data. This works, too, but gives another error when sampling the PP.

Are there perhaps other methods I need to override in the ShiftedLognormal shown above? (And how?)

Any help or ideas would be very welcome

Many thanks