Heteroscedastic trees, ensembles, and other joint models for means and variances

根据数据预测新的结果是任何科学、经济或商业努力的重要组成部分。如今，有许多统计工具可以让人们预测对新情况的平均反应。然而，这些工具还没有被设计来预测变异性，在某些情况下，变异性与预测平均值一样重要。在制造业、软件设计、化学和经济学中，了解未来结果的可变性或波动性通常是数据分析的主要目标。进行这种分析的工具的开发落后于预测手段的开发。

Predicting new outcomes based on data is an important part of any scientific, economic, or business endeavour. There are numerous statistical tools available today that allow people to predict mean, or average, responses to new situations. However, such tools are not yet designed to predict variability, which in some situations is just as important as predicting the average. In manufacturing, software design, chemistry, and economics, understanding how variable or volatile a future outcome may be is often the primary goal of a data analysis. Development of tools for performing this analysis has lagged behind that for predicting means.