Error bounds for Approximations of Markov chains used in Bayesian Sampling
Probability
2018-07-09 v2
Abstract
We give a number of results on approximations of Markov kernels in total variation and Wasserstein norms weighted by a Lyapunov function. The results are applied to examples from Bayesian statistics where approximations to transition kernels are made to reduce computational costs.
Cite
@article{arxiv.1711.05382,
title = {Error bounds for Approximations of Markov chains used in Bayesian Sampling},
author = {James E. Johndrow and Jonathan C. Mattingly},
journal= {arXiv preprint arXiv:1711.05382},
year = {2018}
}