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Using Bayesian inference to understand the allocation of resources between sexual and asexual reproduction


  • C. Jessica E. Metcalf
  • David A. Stephens
  • Mark Rees
  • Svata M. Louda
  • Kathleen H. Keeler


We address the problem of Markov chain Monte Carlo analysis of a complex ecological system by using a Bayesian inferential approach. We describe a complete likelihood framework for the life history of the wavyleaf thistle, including missing information and density dependence. We indicate how, to make inference on life history transitions involving both missing information and density dependence, the stochastic models underlying each component can be combined with each other and with priors to obtain expressions that can be directly sampled. This innovation and the principles described could be extended to other species featuring such missing stage information, with potential for improving inference relating to a range of ecological or evolutionary questions. Copyright (c) 2009 Royal Statistical Society.

Suggested Citation

  • C. Jessica E. Metcalf & David A. Stephens & Mark Rees & Svata M. Louda & Kathleen H. Keeler, 2009. "Using Bayesian inference to understand the allocation of resources between sexual and asexual reproduction," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(2), pages 143-170.
  • Handle: RePEc:bla:jorssc:v:58:y:2009:i:2:p:143-170

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    References listed on IDEAS

    1. S. P. Brooks & E. A. Catchpole & B. J. T. Morgan & M. P. Harris, 2002. "Bayesian methods for analysing ringing data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(1-4), pages 187-206.
    2. Philip Heidelberger & Peter D. Welch, 1983. "Simulation Run Length Control in the Presence of an Initial Transient," Operations Research, INFORMS, vol. 31(6), pages 1109-1144, December.
    3. Julián Horra & M. Rodriguez-Bernal, 2001. "Posterior predictive p-values: what they are and what they are not," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 10(1), pages 75-86, June.
    4. Andrew Gelman, 2003. "A Bayesian Formulation of Exploratory Data Analysis and Goodness-of-fit Testing," International Statistical Review, International Statistical Institute, vol. 71(2), pages 369-382, August.
    5. Hjort, Nils Lid & Dahl, Fredrik A. & Steinbakk, Gunnhildur Hognadottir, 2006. "Post-Processing Posterior Predictive p Values," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1157-1174, September.
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