连续时间数字搜索树和一种边界聚集模型
Continuous time digital search tree and a border aggregation model
摘要：
我们考虑随机数字搜索树的连续时间版本，并构建了一个与Thacker和Volkov（2018）研究的边界聚合模型的耦合，显示了树的高度与聚合所需时间之间的关系。这种关系延续到相应的离散时间模型。因此，我们发现一个非常精确的渐近结果的时间聚集，使用最近的结果Drmota等人。（2020）数字搜索树。
英文摘要：
We consider the continuous-time version of the random digital search tree, and construct a coupling with a border aggregation model as studied in Thacker and Volkov (2018), showing a relation between the height of the tree and the time required for aggregation. This relation carries over to the corresponding discrete-time models. As a consequence we find a very precise asymptotic result for the time to aggregation, using recent results by Drmota et al.\ (2020) for the digital search tree.
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