Quantitative approximations of evolving probability measures and sequential Markov Chain Monte Carlo methods
Probability
2011-12-12 v2
Abstract
We study approximations of evolving probability measures by an interacting particle system. The particle system dynamics is a combination of independent Markov chain moves and importance sampling/resampling steps. Under global regularity conditions, we derive non-asymptotic error bounds for the particle system approximation. In a few simple examples, including high dimensional product measures, bounds with explicit constants of feasible size are obtained. Our main motivation are applications to sequential MCMC methods for Monte Carlo integral estimation.
Cite
@article{arxiv.1010.1696,
title = {Quantitative approximations of evolving probability measures and sequential Markov Chain Monte Carlo methods},
author = {Andreas Eberle and Carlo Marinelli},
journal= {arXiv preprint arXiv:1010.1696},
year = {2011}
}
Comments
28 pages, final version