In order to develop reliable services using machine learning, it is important to understand the uncertainty of the model outputs. Often the probability distribution that the prediction target follows has a complex shape, and a mixture distribution is assumed as a distribution that uncertainty follows. Since the output of mixture density estimation is complicated, its interpretability becomes important when considering its use in real services. In this paper, we propose a method for mixture density estimation that utilizes an interpretable tree structure. Further, a fast inference procedure based on time-invariant information cache achieves both high speed and interpretability.

翻译：为了利用机器学习开发可靠的服务,必须了解模型输出结果的不确定性。预测目标所遵循的概率分布往往具有复杂的形状,混合物分布假定为不确定性所伴随的分布。由于混合物密度估计的输出很复杂,因此在考虑其在实际服务中的使用时,其可解释性变得十分重要。在本文件中,我们提出了一个使用可解释树结构的混合物密度估计方法。此外,基于时间-变化信息缓存的快速推论程序既能达到高速度,也能达到可解释性。