title: Geodesic Monte Carlo on Embedded Manifolds.
abstract: Markov chain Monte Carlo methods explicitly defined on the manifold of probability distributions have recently been established. These methods are constructed from diffusions across the manifold and the solution of the equations describing geodesic flows in the Hamilton-Jacobi representation. This paper takes the differential geometric basis of Markov chain Monte Carlo further by considering methods to simulate from probability distributions that themselves are defined on a manifold, with common examples being classes of distributions describing directional statistics. Proposal mechanisms are developed based on the geodesic flows over the manifolds of support for the distributions, and illustrative examples are provided for the hypersphere and Stiefel manifold of orthonormal matrices.
authors.affiliation: Department of Statistical Science, University College London.
authors.forename: Simon
authors.initials: S
authors.lastname: Byrne
authors.affiliation: Department of Statistical Science, University College London.
authors.forename: Mark
authors.initials: M
authors.lastname: Girolami
keywords: Riemannian manifold; Stiefel manifold; directional statistics; geodesic, Hamiltonian Monte Carlo