  We use the ``map of elections'' approach of Szufa et al. (AAMAS-2020) to
analyze several well-known vote distributions. For each of them, we give an
explicit formula or an efficient algorithm for computing its frequency matrix,
which captures the probability that a given candidate appears in a given
position in a sampled vote. We use these matrices to draw the ``skeleton map''
of distributions, evaluate its robustness, and analyze its properties. Finally,
we develop a general and unified framework for learning the distribution of
real-world preferences using the frequency matrices of established vote
distributions.
