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Psychonomic Bulletin & Review

, Volume 11, Issue 1, pp 192–196 | Cite as

AIC model selection using Akaike weights

  • Eric-Jan WagenmakersEmail author
  • Simon FarrellEmail author
Notes and Comment

Abstract

The Akaike information criterion (AIC; Akaike, 1973) is a popular method for comparing the adequacy of multiple, possibly nonnested models. Current practice in cognitive psychology is to accept a single model on the basis of only the “raw” AIC values, making it difficult to unambiguously interpret the observed AIC differences in terms of a continuous measure such as probability. Here we demonstrate that AIC values can be easily transformed to so-called Akaike weights (e.g., Akaike, 1978, 1979; Bozdogan, 1987; Burnham & Anderson, 2002), which can be directly interpreted as conditional probabilities for each model. We show by example how these Akaike weights can greatly facilitate the interpretation of the results of AIC model comparison procedures.

Keywords

Model Selection Candidate Model Akaike Weight Prior Density Leibler Discrepancy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Psychonomic Society, Inc. 2004

Authors and Affiliations

  1. 1.Northwestern UniversityEvanston

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