Componentwise adaptation for high dimensional MCMC

Author:

Haario Heikki,Saksman Eero,Tamminen Johanna

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability

Reference11 articles.

1. Andrieu, C. & Robert, C. P. (2001), Controlled MCMC for optimal sampling. Preprint. *http://www. statslab. cam. ac.uk/mcmc/

2. Atchade, Y. F. & Rosenthal, J. S. (2003), On Adaptive Markov Chain Monte Carlo Algorithms. Preprint. *http://www. statslab. cam. ac. uk/mcmc/

3. 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 Univ. Press, New York, pp. 599–608.

4. 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.

5. Gilks, W., Roberts, G. & Sahu, S. (1998), ‘Adaptive Markov chain Monte Carlo through regeneration’,J. Am. Stat. Ass. 93, 1045–1054.

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