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arXiv · 1704.07897

Diffeomorphic random sampling using optimal information transport

Martin Bauer·Sarang Joshi·Klas Modin
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Abstract

In this article we explore an algorithm for diffeomorphic random sampling of nonuniform probability distributions on Riemannian manifolds. The algorithm is based on optimal information transport (OIT)---an analogue of optimal mass transport (OMT). Our framework uses the deep geometric connections between the Fisher-Rao metric on the space of probability densities and the right-invariant information metric on the group of diffeomorphisms. The resulting sampling algorithm is a promising alternative to OMT, in particular as our formulation is semi-explicit, free of the nonlinear Monge--Ampere equation. Compared to Markov Chain Monte Carlo methods, we expect our algorithm to stand up well when a large number of samples from a low dimensional nonuniform distribution is needed.

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Martin Bauer, Sarang Joshi, Klas Modin. 2017-04-25. Diffeomorphic random sampling using optimal information transport. https://doi.org/10.1007/978-3-319-68445-1_16

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