When trying to perform classification, we can begin by assuming that
each object should have a uniform chance of being in each category. As
we gather more and more samples, we must be sure to understand that
this is not always the case. We do this by adding "virtual samples"
which means that we pretend we have received M samples that are
uniformly distributed between the categories. As the empirical sample
size approaches and passes the size of M, this model begins to "trust"
its empirical samples more than the virtual samples.
~Kalin Jonas
