Truth, models, model sets, AIC, and multimodel inference: a Bayesian perspective

Journal of Wildlife Management
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Abstract

Statistical inference begins with viewing data as realizations of stochastic processes. Mathematical models provide partial descriptions of these processes; inference is the process of using the data to obtain a more complete description of the stochastic processes. Wildlife and ecological scientists have become increasingly concerned with the conditional nature of model-based inference: what if the model is wrong? Over the last 2 decades, Akaike's Information Criterion (AIC) has been widely and increasingly used in wildlife statistics for 2 related purposes, first for model choice and second to quantify model uncertainty. We argue that for the second of these purposes, the Bayesian paradigm provides the natural framework for describing uncertainty associated with model choice and provides the most easily communicated basis for model weighting. Moreover, Bayesian arguments provide the sole justification for interpreting model weights (including AIC weights) as coherent (mathematically self consistent) model probabilities. This interpretation requires treating the model as an exact description of the data-generating mechanism. We discuss the implications of this assumption, and conclude that more emphasis is needed on model checking to provide confidence in the quality of inference.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Truth, models, model sets, AIC, and multimodel inference: a Bayesian perspective
Series title Journal of Wildlife Management
DOI 10.1002/jwmg.890
Volume 79
Issue 5
Year Published 2015
Language English
Publisher Wildlife Society
Contributing office(s) Patuxent Wildlife Research Center
Description 9 p.
First page 730
Last page 738
Online Only (Y/N) N
Additional Online Files (Y/N) N