Subject of the issue

One of the advantages of the Bayesian network model is that it can handle missing data easily, but the predict methods in pgmpy fail entirely when a row in the input data frame contains None or nan. I have a couple simple changes that support missing data in these predict methods, I'll open a PR with this as the issue. I imagine there are better ways to do this, and I'm happy to take feedback and do this right.