We consider the question of distribution testing (specifically, uniformity and closeness testing) in the streaming setting, \ie under stringent memory constraints. We improve on the results of Diakonikolas, Gouleakis, Kane, and Rao (2019) by providing considerably simpler algorithms, which remove some restrictions on the range of parameters and match their lower bounds.

翻译：我们考虑流式设置中的分布测试问题（具体而言，一致性和接近性测试），即在严格的内存约束下进行。我们通过提供显著简化的算法，改进了Diakonikolas、Gouleakis、Kane和Rao（2019）的结果，该算法移除了对参数范围的某些限制，并匹配了他们的下界。