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Cited by 417

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Adaptive proposal distribution for random walk Metropolis algorithm

Open access
Haario, Heikki, Saksman, Eero, Tamminen, Johanna·1999·Computational Statistics
DOI 10.1007/s001800050022
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Show all 3 references

Research works printed but not linked: 14

  1. Gelfand, A. E. & Smith, A. F. M. (1990), ‘Sampling-based approaches to calculate marginal densities’, J. Am. Stat. Ass. 85, 853–409.
  2. Gelman, A. G., Roberts, G. O. & Gilks, W. R. (1996), Efficient Metropolis jumping rules, in J. M. Bernardo, J. O. Berger, A. F. David & A. F. M. Smith, eds, ‘Bayesian Statistics V’, Oxford University press, Oxford, pp. 599–608. · DOI · Paper page
  3. Gilks, W., Best, N. & Tan, K. (1995), ‘Adaptive rejection Metropolis sampling’, Applied Statistics 44, 455–472. · DOI · Paper page
  4. Gilks, W. R., Richardson, S. & Spiegelhalter, D. J. (1995), Introducing Markov chain Monte Carlo, in W. R. Gilks, S. Richardson & D. J. Spiegelhalter, eds, ‘Markov Chain Monte Carlo in Practice’, Chapman & Hall, pp. 1–19. · DOI · Paper page
  5. Gilks, W. R., Roberts, G. O. & George, E. I. (1994), ‘Adaptive direction sampling’, Statistical Science 43, 179–189.
  6. Gilks, W. & Roberts, G. (1995), Stategies for improving MCMC, in W. R. Gilks, S. Richardson & D. J. Spiegelhalter, eds, ‘Markov Chain Monte Carlo in Practice’, Chapman & Hall, pp. 75–88.
  7. Gilks, W., Roberts, G. & Sahu, S. (1998), ‘Adaptive Markov chain Monte Carlo through regeneration’, J. Amer. Statist. Assoc. To appear. · DOI
  8. Gilks, W. & Wild, P. (1992), ‘Adaptive rejection sampling for Gibbs sampling’, Applied Statistics 41, 337–348. · DOI · Paper page
  9. Haario, H., Saksman, E. & Tamminen, J. (1998a), Adaptive proposal distribution for random walk Metropolis algorithm. Reports of the Department of Mathematics, University of Helsinki, Preprint 176.
  10. Haario, H., Saksman, E. & Tamminen, J. (1998b), An Adaptive Metropolis algorithm. Reports of the Department of Mathematics, University of Helsinki, Preprint 187.
  11. Kyrölä, E., Sihvola, E., Kotivuori, Y., Tikka, M., Tuomi, T. & Haario, H. (1993), ‘Inverse Theory for Occultation Measurements, 1. Spectral Inversion’, J. Geophys. Res. 98, 7367–7381. · doi:10.1029/92jd02678
  12. Press, W. H., Teukolsky, S. A., Vetterling, W. T. & Flannery, B. P. (1992), Numerical Recipes in FORTRAN, The art of scientific computing, Clasendon Press, Oxford.
  13. Sahu, S. K. & Zhigljavsky, A. A. (1998a), Self regenerative Markov chain Monte Carlo. Preprint. URL: https://doi.org/www.stats.bris.ac.uk/MCMC/
  14. Sahu, S. K. & Zhigljavsky, A. A. (1998b), Adaptation for self regenerative MCMC. Preprint. URL: https://doi.org/www.stats.bris.ac.uk/MCMC/

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Metadata

Authors
Haario, H., Saksman, E., & Tamminen, J.
Venue
Computational Statistics
Publisher
Springer Science and Business Media LLC
Published
1999-09
Volume
14
Issue
3
Pages
375-395
ISSN
  • 0943-4062 (ISSN-L)
  • 1613-9658
DOI
10.1007/s001800050022
OpenAlex
W1971670438
Cited by
417
References
3
FWCI
3.26
Type
journal article
Language
en
Open access
green
Licence
www.springer.com/tdm (tdm)

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CoChat. (2026). Citation check: Adaptive proposal distribution for random walk Metropolis algorithm. https://app.cochat.ai/papers/10.1007/s001800050022
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Haario, H., Saksman, E., & Tamminen, J. (1999). Adaptive proposal distribution for random walk Metropolis algorithm. Computational Statistics. https://doi.org/10.1007/s001800050022

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