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Using Bayesian inference to understand the allocation of resources between sexual and asexual reproduction
Author(s) -
Metcalf C. Jessica E.,
Stephens David A.,
Rees Mark,
Louda Svata M.,
Keeler Kathleen H.
Publication year - 2009
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2008.00652.x
Subject(s) - inference , markov chain monte carlo , bayesian inference , computer science , bayesian probability , statistical inference , prior probability , range (aeronautics) , artificial intelligence , machine learning , ecology , mathematics , statistics , biology , engineering , aerospace engineering
Summary. 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.