It seems that the cdf for most distributions works, e.g.,

```
import torch
from torch.distributions.laplace import Laplace
m = Laplace(torch.tensor([0.0]), torch.tensor([1.0]))
m.cdf(-0.0886)
```

However, for Beta distributions, it does not seem to be implemented yet:

```
from torch.distributions.beta import Beta
m = Beta(torch.tensor([0.5]), torch.tensor([0.5]))
m.cdf(0.0029)
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-10-b5e9a04733d4> in <module>
2
3 m = Beta(torch.tensor([0.5]), torch.tensor([0.5]))
----> 4 m.cdf(0.0029)
~/miniconda3/lib/python3.7/site-packages/torch/distributions/distribution.py in cdf(self, value)
152 value (Tensor):
153 """
--> 154 raise NotImplementedError
155
156 def icdf(self, value):
NotImplementedError:
```

I was searching for GitHub PRs/Issues but couldn’t find any. Does anyone have any tips regarding an efficient implementation in PyTorch for this? I was wondering, maybe someone implemented this already in a numerically stable way or so. Any pointers would be appreciated!