Stochastic billiards for sampling from the boundary of a convex set
Probability
2014-10-22 v1 Data Structures and Algorithms
Abstract
Stochastic billiards can be used for approximate sampling from the boundary of a bounded convex set through the Markov Chain Monte Carlo (MCMC) paradigm. This paper studies how many steps of the underlying Markov chain are required to get samples (approximately) from the uniform distribution on the boundary of the set, for sets with an upper bound on the curvature of the boundary. Our main theorem implies a polynomial-time algorithm for sampling from the boundary of such sets.
Keywords
Cite
@article{arxiv.1410.5775,
title = {Stochastic billiards for sampling from the boundary of a convex set},
author = {A. B. Dieker and Santosh Vempala},
journal= {arXiv preprint arXiv:1410.5775},
year = {2014}
}