Explicit error bounds for lazy reversible Markov chain Monte Carlo

@article{Rudolf2009ExplicitEB,
  title={Explicit error bounds for lazy reversible Markov chain Monte Carlo},
  author={Daniel Rudolf},
  journal={J. Complexity},
  year={2009},
  volume={25},
  pages={11-24}
}
We prove explicit, i.e., non-asymptotic, error bounds for Markov Chain Monte Carlo methods, such as the Metropolis algorithm. The problem is to compute the expectation (or integral) of f with respect to a measure π which can be given by a density ̺ with respect to another measure. A straight simulation of the desired distribution by a random number generator is in general not possible. Thus it is reasonable to use Markov chain sampling with a burn-in. We study such an algorithm and extend the… CONTINUE READING
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