Model inference or model selection: discussion of Klugkist, Laudy, and Hoijtink (2005).
I. Klugkist, O. Laudy, and H. Hoijtink (2005) presented a Bayesian approach to analysis of variance models with inequality constraints. Constraints may play 2 distinct roles in data analysis. They may represent prior information that allows more precise inferences regarding parameter values, or they may describe a theory to be judged against the data. In the latter case, the authors emphasized the use of Bayes factors and posterior model probabilities to select the best theory. One difficulty is that interpretation of the posterior model probabilities depends on which other theories are included in the comparison. The posterior distribution of the parameters under an unconstrained model allows one to quantify the support provided by the data for inequality constraints without requiring the model selection framework.
Stern
Hal S
HS
Department of Statistics, University of California, Irvine, Irvine, CA 92967-1250, USA. sternh@uci.edu
D000704:Analysis of Variance; D001499:Bayes Theorem; D006801:Humans; D016008:Statistical Distributions