Error bounds for Approximations of Markov chains used in Bayesian Sampling

Understand

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.