Many nonlinear hierarchical models include linear model components. The Weibull, for example, is a suitable nonlinear model for response time. A linear models may be placed on the log of the scale parameter to account for person, item, and condition effects. We have advocated linear models that account for dispersion through the inclusion of additional noise terms, even when identifiability is dependent on the choice of the prior. We show how these additional noise terms simplify sampling, vastly reduce autocorrelation in MCMC outputs, and provide for convenient computation of Bayes factors. |