Mixing categorial and continuous data in Naive Bayes classifier using scikit-learn
You will need the following steps:

Calculate the probability from the categorical variables (using predict_proba method from BernoulliNB)
Calculate the probability from the continuous variables (using predict_proba method from GaussianNB)
Multiply 1. and 2. AND
Divide by the prior (either from BernoulliNB or from GaussianNB since they are the same) AND THEN
Divide 4. by the sum (over the classes) of 4. This is the normalisation step.

It should be easy enough to see how you can add your own prior instead of using those learned from the data.
I'm using scikit-learn in Python to develop a classification algorithm to predict the gender of certain customers. Amongst others, I want to use the Naive Bayes classifier but my problem is that I have a mix of categorical data (ex: "Registered online", "Accepts email notifications" etc) and continuous data (ex: "Age", "Length of membership" etc). I haven't used scikit much before but I suppose that that Gaussian Naive Bayes is suitable for continuous data and that Bernoulli Naive Bayes can be used for categorical data. However, since I want to have both categorical and continuous data in my model, I don't really know how to handle this. Any ideas would be much appreciated!