Abstract and Keywords
According to the principle of parsimony, model selection methods should value both descriptive accuracy and simplicity. Here we focus primarily on Bayes factors and minimum description length, explaining how these procedures strike a balance between goodness-of-fit and parsimony. Throughout, we demonstrate the methods with an application on false memory, evaluating three competing multimonial proces tree models of interference in memory.
Keywords: model selection, goodness of fit, parsimony, inference, Akaike's information criterion (AIC), Bayesian information criterion (BIC), minimum description length (MDL), Bayes factor (BF), Akaike weights, Jeffreys weights, Rissanen weights, Schwartz weights
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