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On the Flexibility of Metropolis–Hastings Acceptance Probabilities in Auxiliary Variable Proposal Generation

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  • GEIR STORVIK

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  • Geir Storvik, 2011. "On the Flexibility of Metropolis–Hastings Acceptance Probabilities in Auxiliary Variable Proposal Generation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 38(2), pages 342-358, June.
    • Handle: RePEc:bla:scjsta:v:38:y:2011:i:2:p:342-358
    DOI: j.1467-9469.2010.00709.x
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    File URL: http://hdl.handle.net/10.1111/j.1467-9469.2010.00709.x
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    Cited by:

    1. 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.
    2. Luca Martino & Jesse Read, 2013. "On the flexibility of the design of multiple try Metropolis schemes," Computational Statistics, Springer, vol. 28(6), pages 2797-2823, December.
    3. Xin Luo & Håkon Tjelmeland, 2019. "A multiple-try Metropolis–Hastings algorithm with tailored proposals," Computational Statistics, Springer, vol. 34(3), pages 1109-1133, September.
    4. Martino, Luca & Del Olmo, Victor Pascual & Read, Jesse, 2012. "A multi-point Metropolis scheme with generic weight functions," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1445-1453.
    5. Takashi Kamihigashi & Hiroyuki Watanabe, 2016. "A Multiple-Try Extension of the Particle Marginal Metropolis-Hastings (PMMH) Algorithm with an Independent Proposal," Discussion Paper Series DP2016-36, Research Institute for Economics & Business Administration, Kobe University.

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