  Categorical random variables are a common staple in machine learning methods
and other applications across disciplines. Many times, correlation within
categorical predictors exists, and has been noted to have an effect on various
algorithm effectiveness, such as feature ranking and random forests. We present
a mathematical construction of a sequence of identically distributed but
dependent categorical random variables, and give a generalized multinomial
distribution to model the probability of counts of such variables.
