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Improving the acceptance rate of reversible jump MCMC proposals

Author

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  • Al-Awadhi, Fahimah
  • Hurn, Merrilee
  • Jennison, Christopher

Abstract

Recent articles have commented on the difficulty of proposing efficient reversible jump moves within MCMC. We suggest a new way to make proposals more acceptable using a secondary Markov chain to modify proposed moves--at little extra programming cost.

Suggested Citation

  • Al-Awadhi, Fahimah & Hurn, Merrilee & Jennison, Christopher, 2004. "Improving the acceptance rate of reversible jump MCMC proposals," Statistics & Probability Letters, Elsevier, vol. 69(2), pages 189-198, August.
    • Handle: RePEc:eee:stapro:v:69:y:2004:i:2:p:189-198
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    References listed on IDEAS

    as
    1. Hum, Merrilee A. & Rue, Håvard & Sheehan, Nuala A., 1999. "Block updating in constrained Markov chain Monte Carlo sampling," Statistics & Probability Letters, Elsevier, vol. 41(4), pages 353-361, February.
    2. repec:dau:papers:123456789/6040 is not listed on IDEAS
    3. Olivier Cappé & Christian P. Robert & Tobias Rydén, 2003. "Reversible jump, birth‐and‐death and more general continuous time Markov chain Monte Carlo samplers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(3), pages 679-700, August.
    4. Fahimah Al‐Awadhi & Christopher Jennison & Merrilee Hurn, 2004. "Statistical image analysis for a confocal microscopy two‐dimensional section of cartilage growth," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(1), pages 31-49, January.
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    Cited by:

    1. Gagnon, Philippe & Bédard, Mylène & Desgagné, Alain, 2019. "Weak convergence and optimal tuning of the reversible jump algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 161(C), pages 32-51.
    2. Alexander Meyer-Gohde & Daniel Neuhoff, 2015. "Generalized Exogenous Processes in DSGE: A Bayesian Approach," SFB 649 Discussion Papers SFB649DP2015-014, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Hubin, Aliaksandr & Storvik, Geir, 2018. "Mode jumping MCMC for Bayesian variable selection in GLMM," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 281-297.
    4. Leonardo Oliveira Martins & Hirohisa Kishino, 2010. "Distribution of distances between topologies and its effect on detection of phylogenetic recombination," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 145-159, February.
    5. Oedekoven, C.S. & King, R. & Buckland, S.T. & Mackenzie, M.L. & Evans, K.O. & Burger, L.W., 2016. "Using hierarchical centering to facilitate a reversible jump MCMC algorithm for random effects models," Computational Statistics & Data Analysis, Elsevier, vol. 98(C), pages 79-90.
    6. Chen, Langnan & Luo, Jiawen & Liu, Hao, 2013. "The determinants of liquidity with G-RJMCMC-VS model: Evidence from China," Economic Modelling, Elsevier, vol. 35(C), pages 192-198.
    7. David I. Hastie & Peter J. Green, 2012. "Model choice using reversible jump Markov chain Monte Carlo," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(3), pages 309-338, August.
    8. Rosineide Fernando da Paz & Jorge Luis Bazán & Luis Aparecido Milan, 2017. "Bayesian estimation for a mixture of simplex distributions with an unknown number of components: HDI analysis in Brazil," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(9), pages 1630-1643, July.

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