An Efficient Implementation of Riemannian Manifold Hamiltonian Monte Carlo for Gaussian Process Models
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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 ∈ R^N is drawn from a Gaussian Process (GP) prior, and observations y_n are independent given x_n. Sufficient technical and algorithmic details are provided for the implementation of RMHMC for distributions arising from GP priors.
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