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This technical report presents pseudo-code for a Riemannian manifoldHamiltonian Monte Carlo (RMHMC) method to efficiently simulate samples from$N$-dimensional posterior distributions $p(x|y)$, where $x \in R^N$ is drawnfrom a Gaussian Process (GP) prior, and observations $y_n$ are independentgiven $x_n$. Sufficient technical and algorithmic details are provided for theimplementation of RMHMC for distributions arising from GP priors.

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