I’m a new user. Pyro seems like a very useful tool, but I’m having trouble handling missing data. I’ve want to use poutine.mask to ignore missing data as in done in the answer to this question: Modeling missingness indicators.
When I try doing this in the SVI tutorial, I get a type error, but my mask is definitely of type torch.uint8, not NoneType
ValueError: Expected mask to be a boolean but got <class ‘NoneType’>
latent_fairness dist |
Here’s a colab link to the modified tutorial: https://colab.research.google.com/drive/1uIKG9qv-u3moMwOk-Mma5rAHaufScnvG#scrollTo=0kifRoD9nbWw
Am I using poutine.mask correctly?