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Computational Statistics

, Volume 20, Issue 2, pp 265–273 | Cite as

Componentwise adaptation for high dimensional MCMC

  • Heikki Haario
  • Eero Saksman
  • Johanna Tamminen
Article

Summary

We introduce a new adaptive MCMC algorithm, based on the traditional single component Metropolis-Hastings algorithm and on our earlier adaptive Metropolis algorithm (AM). In the new algorithm the adaption is performed component by component. The chain is no more Markovian, but it remains ergodic. The algorithm is demonstrated to work well in varying test cases up to 1000 dimensions.

Keywords

MCMC adaptive MCMC Metropolis-Hastings algorithm 

Notes

Acknowledgments

This work has been supported by the Academy of Finland, MaDaMe project. We would also like to thank Prof. P.J. Green for the code for computing the integrated autocorrelation values.

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Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • Heikki Haario
    • 1
  • Eero Saksman
    • 2
  • Johanna Tamminen
    • 3
  1. 1.University of Helsinki Department of Mathematics and StatisticsUniversity of HelsinkiFinland
  2. 2.University of Jyväskylä Department of Mathematics and StatisticsUniversity of JyväskyläFinland
  3. 3.Finnish Meteorological Institute Geophysical Research DivisionHelsinkiFinland

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