Took a while to figure out what's going on for TADM.

TADM learns a maximum entropy model, which yields the event probabilities matching with the empirical probabilities in the input event space. However, the probabilities associated with features may not be matching with those in the input file.

Can I set the TADM to fit a model that generates the feature probabilities matching with those in the input file?

This is like a constraint satisfying model learning problem. Each feature and its empirical probability is viewed as a constraint on the underlying unknown model.

Thanks!

brook