I'm trying to model the probability of an event Y based on three independant variables, one (X) is continuous (a log count) and the others (A and B) are categorical (nominal). B is a subcategory of A. A has 4 levels, most of all are well populated, B has 3 to 15 levels, depending of level of A, and about half are well populated.

I could take all my three variables and do a Bayesian logistic regression (one-hot encoding for A and B, ending up with 1+4+15 columns). I could also proceed by steps: four distinct models/logistic regressions of Y based on X, one for each level of A. Then using the coefficients of each as priors on X, do logistic regressions Y ~ X on each level of B (if levelBj of B belongs to levelAi of A then I use the priors of model i above).

Does it make sense to proceed that way? Are there advantages/disadvantages doing that? Are there alternatives? Any links/tutorials on the problem of mixing categorical and continuous variables for bayesian logistic regression are also appreciated (particularly for PyMC3).


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.