We study the growth of a time-ordered rooted tree by probabilistic attachment of new vertices to leaves. We construct a likelihood function of the leaves based on the connectivity of the tree. We take such connectivity to be induced by the merging of directed ordered paths from leaves to the root. Combining the likelihood with an assigned prior distribution leads to a posterior leaf distribution from which we sample attachment points for new vertices. We present computational examples of such Bayesian tree growth. Although the discussion is generic, the initial motivation for the paper is the concept of a distributed ledger, which may be regarded as a time-ordered random tree that grows by probabilistic leaf attachment.

翻译：我们通过将新脊椎与叶子相依的概率性结合,研究有时间顺序的根植树的增长。我们根据树的连接性,构建叶子的可能功能。我们认为,这种连接性是通过将有方向的定线路径从叶子与根茎相结合而诱发的。将这种可能性与先前分配的可能性结合起来,就会导致后叶分布,我们从中抽取新脊椎的附加点。我们提出了这种巴伊西亚树生长的计算例子。虽然讨论是通用的,但该文件的最初动机是分配分类账的概念,它可被视为按时间顺序排列的随机树,通过概率叶附加而生长。