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A Tutorial on Reversible Jump MCMC with a View toward Applications in QTL‐mapping

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  • Rasmus Waagepetersen
  • Daniel Sorensen

Abstract

A tutorial derivation of the reversible jump Markov chain Monte Carlo (MCMC) algorithm is given. Various examples illustrate how reversible jump MCMC is a general framework for Metropolis‐Hastings algorithms where the proposal and the target distribution may have densities on spaces of varying dimension. It is finally discussed how reversible jump MCMC can be applied in genetics to compute the posterior distribution of the number, locations, effects, and genotypes of putative quantitative trait loci.

Suggested Citation

  • Rasmus Waagepetersen & Daniel Sorensen, 2001. "A Tutorial on Reversible Jump MCMC with a View toward Applications in QTL‐mapping," International Statistical Review, International Statistical Institute, vol. 69(1), pages 49-61, April.
    • Handle: RePEc:bla:istatr:v:69:y:2001:i:1:p:49-61
    DOI: 10.1111/j.1751-5823.2001.tb00479.x
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    Cited by:

    1. Koop, Gary & Leon-Gonzalez, Roberto & Strachan, Rodney, 2012. "Bayesian model averaging in the instrumental variable regression model," Journal of Econometrics, Elsevier, vol. 171(2), pages 237-250.
    2. Leon-Gonzalez, Roberto & Scarpa, Riccardo, 2008. "Improving multi-site benefit functions via Bayesian model averaging: A new approach to benefit transfer," Journal of Environmental Economics and Management, Elsevier, vol. 56(1), pages 50-68, July.
    3. 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.

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