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El Uso de Promedios de Modelo Bayesiano para Mejorar la Representación de la Incertidumbre en Modelos Ecológicos
Author(s) -
WINTLE B. A.,
McCARTHY M. A.,
VOLINSKY C. T.,
KAVANAGH R. P.
Publication year - 2003
Publication title -
conservation biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.2
H-Index - 222
eISSN - 1523-1739
pISSN - 0888-8892
DOI - 10.1111/j.1523-1739.2003.00614.x
Subject(s) - bayesian inference , model selection , bayesian probability , range (aeronautics) , uncertainty analysis , selection (genetic algorithm) , computer science , sensitivity analysis , inference , econometrics , uncertainty quantification , prediction interval , statistical inference , ecology , machine learning , statistics , artificial intelligence , mathematics , materials science , composite material , biology
In conservation biology, uncertainty about the choice of a statistical model is rarely considered. Model‐selection uncertainty occurs whenever one model is chosen over plausible alternative models to represent understanding about a process and to make predictions about future observations. The standard approach to representing prediction uncertainty involves the calculation of prediction (or confidence) intervals that incorporate uncertainty about parameter estimates contingent on the choice of a “best” model chosen to represent truth. However, this approach to prediction based on statistical models tends to ignore model‐selection uncertainty, resulting in overconfident predictions. Bayesian model averaging (BMA) has been promoted in a range of disciplines as a simple means of incorporating model‐selection uncertainty into statistical inference and prediction. Bayesian model averaging also provides a formal framework for incorporating prior knowledge about the process being modeled. We provide an example of the application of BMA in modeling and predicting the spatial distribution of an arboreal marsupial in the Eden region of southeastern Australia. Other approaches to estimating prediction uncertainty are discussed.