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

arXiv: https://arxiv.org/abs/1810.11893 · HTML: https://mlsanity.com/paper/1810.11893/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2018.

## Built on by (most cited first, 1 of 1)

- [Lagrangian Manifold Monte Carlo on Monge Patches](https://mlsanity.com/paper/2202.00755/) (2022)

## Abstract (arXiv)

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.