A Guide to Exact Simulation

@inproceedings{Dimakos2000AGT,
  title={A Guide to Exact Simulation},
  author={Xeni K. Dimakos},
  year={2000}
}
Markov Chain Monte Carlo (MCMC) methods are used to sample from complicated multivariate distributions with normalizing constants that may not be computable and from which direct sampling is not feasible. A fundamental problem is to determine convergence of the chains. Propp & Wilson (1996) devised a Markov chain algorithm called Coupling From The Past (CFTP) that solves this problem, as it produces exact samples from the target distribution and determines automatically how long it needs to run… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 24 references

Implementing MCMC. In Markov chain Monte-Carlo

  • A. Raftery, S. Lewis
  • 1996
Highly Influential
5 Excerpts

An extension of Fill's exact sampling algorithm

  • D J.Murdoch., J S.Rosenthal.
  • 1998

E cient Exact Sampling From the Ising Model Using Swendsen-Wang

  • M. Huber
  • 1998
1 Excerpt

MCMC Convergence Diag

  • K. Mengersen, C. Robert, C. Guihenneuc-Jouyaux
  • 1998
1 Excerpt

Perfect sampling from independent Metropolis

  • J N.Corcoran., R L.Tweedie.
  • 1998

Perfect simulation in stochastic geometry

  • W S.Kendall., E. Thonnes
  • 1998

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