Evaluation of multilevel decision trees

An alternative to full nested evaluation is to perform a random sample of evaluations and use statistical methods to perform inference about the entire tree. We show that the most natural estimate is biased and consider two alternatives: the parametric bootstrap and hierarchical Bayes inference. We explore the properties of these inferences through a simulation study.