Controlled MCMC for Optimal Sampling

@inproceedings{Andrieu2001ControlledMF,
  title={Controlled MCMC for Optimal Sampling},
  author={Christophe Andrieu},
  year={2001}
}
In this paper we develop an original and general framework for automatically optimizing the statistical properties of Markov chain Monte Carlo (MCMC) samples, which are typically used to evaluate complex integrals. The Metropolis-Hastings algorithm is the basic building block of classical MCMC methods and requires the choice of a proposal distribution, which usually belongs to a parametric family. The correlation properties together with the exploratory ability of the Markov chain heavily… CONTINUE READING
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References

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On the Convergence

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Implementation and Per

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1 Excerpt

The Use of Multiple-Try Method

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Posterior Simulation

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