The implications for Bayesian optimization are similar to the lessons learned from random optimization: changing hyperparameters can change the optimized process, meaning one must analyze the character of the eigenvectors to ensure one is optimizing the same processes. The lag time and number of scored eigenvectors do not affect model selection.