We introduce Joint Probability Trees (JPT), a novel approach that makes learning of and reasoning about joint probability distributions tractable for practical applications. JPTs support both symbolic and subsymbolic variables in a single hybrid model, and they do not rely on prior knowledge about variable dependencies or families of distributions. JPT representations build on tree structures that partition the problem space into relevant subregions that are elicited from the training data instead of postulating a rigid dependency model prior to learning. Learning and reasoning scale linearly in JPTs, and the tree structure allows white-box reasoning about any posterior probability $P(Q|E)$, such that interpretable explanations can be provided for any inference result. Our experiments showcase the practical applicability of JPTs in high-dimensional heterogeneous probability spaces with millions of training samples, making it a promising alternative to classic probabilistic graphical models.

我们介绍了一种新的方法--联合概率树(JPT)，它可以让学习和推理关于联合概率分布变得可行，支持符号和子符号变量，不需要先验知识，能够构建一个树结构，主要是通过训练数据 elicited来建立子区域的关联性，最终发现JPTs可以是一个很有前途的概率图模型替代方案。