Hoeffding Bounds for Markov Chains

@inproceedings{Leon2008HoeffdingBF,
  title={Hoeffding Bounds for Markov Chains},
  author={Carlos A Tache Leon and François Perron},
  year={2008}
}
f dπ. From the weak law of large numbers we know that the empirical mean nSn = n −1∑n k=1 f(Xk) converges to μ in probability. This result is the working principle behind all Markov chain Monte Carlo (MCMC) integration techniques. The basis of MCMC dates back to the 50’s with the article of Metropolis, Rosenbluth, Rosenbluth, Teller and Teller (1953), but it is only with today’s computing power that these methods can give their full measure. Like in the classical Monte Carlo schemes, one way of… CONTINUE READING
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