# Wasserstein-based methods for convergence complexity analysis of MCMC with application to Albert and Chib's algorithm

@article{Qin2018WassersteinbasedMF, title={Wasserstein-based methods for convergence complexity analysis of MCMC with application to Albert and Chib's algorithm}, author={Qian Qin and J. Hobert}, journal={arXiv: Statistics Theory}, year={2018} }

Over the last 25 years, techniques based on drift and minorization (d&m) have been mainstays in the convergence analysis of MCMC algorithms. However, results presented herein suggest that d&m may be less useful in the emerging area of convergence complexity analysis, which is the study of how Monte Carlo Markov chain convergence behavior scales with sample size, $n$, and/or number of covariates, $p$. The problem appears to be that minorization becomes a serious liability as dimension increases… Expand

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