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Convergence Speed and Approximation Accuracy of Numerical MCMC
[article]
2022
arXiv
pre-print
When implementing Markov Chain Monte Carlo (MCMC) algorithms, perturbation caused by numerical errors is sometimes inevitable. This paper studies how perturbation of MCMC affects the convergence speed and Monte Carlo estimation accuracy. Our results show that when the original Markov chain converges to stationarity fast enough and the perturbed transition kernel is a good approximation to the original transition kernel, the corresponding perturbed sampler has similar convergence speed and high
arXiv:2203.03104v1
fatcat:e4firz7ffvgkhaegoo4o2lvvcy