# Central limit theorems for Markov chains based on their convergence rates in Wasserstein distance

@article{Jin2020CentralLT, title={Central limit theorems for Markov chains based on their convergence rates in Wasserstein distance}, author={Rui Jin and Aixin Tan}, journal={arXiv: Statistics Theory}, year={2020} }

Many tools are available to bound the convergence rate of Markov chains in total variation (TV) distance. Such results can be used to establish central limit theorems (CLT) that enable error evaluations of Monte Carlo estimates in practice. However, convergence analysis based on TV distance is often non-scalable to high-dimensional Markov chains (Qin and Hobert (2018); Rajaratnam and Sparks (2015)). Alternatively, robust bounds in Wasserstein distance are often easier to obtain, thanks to a… Expand

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