I played with the implemented multinomial distribution found this unexpected behavior of

```
import numpy as np
import pymc3 as pm
np.exp(pm.Multinomial.dist(n = 1, p = np.array([0, 1])).logp(np.array([0, 1])).eval())
```

My PyMC3 3.4.1 returns `array([nan])`

even know by definition this should be

However this

```
np.exp(pm.Multinomial.dist(n = 2, p = np.array([0, 1])).logp(np.array([1, 1])).eval())
```

correctly returns `array([0.])`

.

I think one can work around this issue by sub setting p and x to the values where p \ne 0 or x \ne 0 but it would be nicer if this is done automatically.