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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. © 2009 Royal Statistical Society.

Original publication

DOI

10.1111/j.1467-9876.2008.00652.x

Type

Journal article

Journal

Journal of the Royal Statistical Society. Series C: Applied Statistics

Publication Date

01/05/2009

Volume

58

Pages

143 - 170