Abstract
Steinschneider et al. (2017) investigate model choices made in the hierarchical climate reconstruction approach of Schofield et al. (2016). We identify two flaws in their approach. The first is the use of an unusual approximation to Bayesian inference that unnecessarily discards important information. The second is that they mischaracterize the robustness of their reconstructions due to overlooking important features of the out-of-sample predictions. We demonstrate how full Bayesian inference can be conducted with no additional effort, providing R/JAGS code. We also show how graphical visualization of the out-of-sample predictions can lead to better understanding and comparison of the models fitted.