  Due to its constrained support, the Dirichlet distribution is uniquely suited
to many applications. The constraints that make it powerful, however, can also
hinder practical implementations, particularly those utilizing Markov Chain
Monte Carlo (MCMC) techniques such as Hamiltonian Monte Carlo. I demonstrate a
series of transformations that reshape the canonical Dirichlet distribution
into a form much more amenable to MCMC algorithms.
