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

Cruising The Simplex: Hamiltonian Monte Carlo and the Dirichlet Distribution