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

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)143-170
Number of pages28
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume58
Issue number2
DOIs
StatePublished - May 1 2009
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Density dependence
  • Ecological system
  • Markov chain Monte Carlo sampling
  • Missing data

Fingerprint Dive into the research topics of 'Using Bayesian inference to understand the allocation of resources between sexual and asexual reproduction'. Together they form a unique fingerprint.

  • Cite this