Abstract
We consider a strategy for Bayesian model building that begins by fitting a simple, default model to the data. Numerical and graphical exploratory tools, based on summary quantities from the default fit, are used to assess the adequacy of the initial model and to identify directions in which the fit can be refined. We apply this strategy to build a Bayesian regression model for a classic set of data on brain and body weights of mammalian species. We discover inadequacies in the traditional regression model through use of our exploratory tools. More sophisticated models point the way toward judging the adequacy of a theory on the relationship between body weight and brain weight, and also bear on the timeless question “do we have big brains?”
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MacEachern, S.N., Peruggia, M. (2002). Bayesian Tools for EDA and Model Building: A Brainy Study. In: Gatsonis, C., et al. Case Studies in Bayesian Statistics. Lecture Notes in Statistics, vol 162. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-0035-9_9
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DOI: https://doi.org/10.1007/978-1-4613-0035-9_9
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-95169-0
Online ISBN: 978-1-4613-0035-9
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