An Efficient Implementation of Riemannian Manifold Hamiltonian Monte Carlo for Gaussian Process Models

This technical report presents pseudo-code for a Riemannian manifold Hamiltonian Monte Carlo (RMHMC) method to efficiently simulate samples from N-dimensional posterior distributions p(x|y), where x ∈ RN is drawn from a Gaussian Process (GP) prior, and observations yn are independent given xn. Sufficient technical and algorithmic details are provided for the implementation of RMHMC for distributions arising from GP priors.