  Markov Chain Monte Carlo (MCMC) is an invaluable means of inference with
complicated models, and Hamiltonian Monte Carlo, in particular Riemannian
Manifold Hamiltonian Monte Carlo (RMHMC), has demonstrated impressive success
in many challenging problems. Current RMHMC implementations, however, rely on a
Riemannian metric that limits their application to analytically-convenient
models. In this paper I propose a new metric for RMHMC without these
limitations and verify its success on a distribution that emulates many
hierarchical and latent models.
