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

Ulrich Paquet; Marco Fraccaro

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arXiv · 1810.11893

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

Ulrich Paquet·Marco Fraccaro

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Abstract

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 $x \in R^N$ 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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Ulrich Paquet, Marco Fraccaro. 2018-10-28. An Efficient Implementation of Riemannian Manifold Hamiltonian Monte Carlo for Gaussian Process Models. https://arxiv.org/abs/1810.11893

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