Non-centered Gaussian random-effect parameterization (${\alpha }_i=\delta +\tau z_i,z_i\sim N(0,1)$)

A normal local effect ${\alpha }_i\mid \delta ,\tau \sim N(\delta ,{\tau }^2)$ can be represented by a standardized latent variable $z_i\sim N(0,1)$ independent of the hyperparameters, with ${\alpha }_i=\delta +\tau z_i$. This preserves the hierarchical model while changing the coordinates explored by Markov chain Monte Carlo. It often improves mixing when individual effects are weakly informed, reducing strong scale-effect dependence. Centered coordinates can be better with very informative individual data. The stochastic prior on $z_i$ implements the transformation correctly; re-expressing an existing density directly instead requires the appropriate Jacobian determinant.