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The neglected tool in the Bayesian ecologist's shed: a case study testing informative priors' effect on model accuracy
Author(s) -
Morris William K.,
Vesk Peter A.,
McCarthy Michael A.,
Bunyavejchewin Sarayudh,
Baker Patrick J.
Publication year - 2015
Publication title -
ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.17
H-Index - 63
ISSN - 2045-7758
DOI - 10.1002/ece3.1346
Subject(s) - prior probability , bayesian probability , computer science , prior information , machine learning , artificial intelligence , econometrics , statistics , mathematics
Despite benefits for precision, ecologists rarely use informative priors. One reason that ecologists may prefer vague priors is the perception that informative priors reduce accuracy. To date, no ecological study has empirically evaluated data‐derived informative priors' effects on precision and accuracy. To determine the impacts of priors, we evaluated mortality models for tree species using data from a forest dynamics plot in Thailand. Half the models used vague priors, and the remaining half had informative priors. We found precision was greater when using informative priors, but effects on accuracy were more variable. In some cases, prior information improved accuracy, while in others, it was reduced. On average, models with informative priors were no more or less accurate than models without. Our analyses provide a detailed case study on the simultaneous effect of prior information on precision and accuracy and demonstrate that when priors are specified appropriately, they lead to greater precision without systematically reducing model accuracy.

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