In this letter, we introduce a new approach to quantify the closeness of symbolic sequences and test it in the framework of the authorship attribution problem. The method, based on a recently discovered urn representation of the Pitman-Yor process, is highly accurate compared to other state-of-the-art methods, featuring a substantial gain in computational efficiency and theoretical transparency. Our work establishes a clear connection between urn models critical in interpreting innovation processes and nonparametric Bayesian inference. It opens the way to design more efficient inference methods in the presence of complex correlation patterns and non-stationary dynamics.

翻译：本文提出一种新的符号序列接近度量化方法，并在作者归属问题框架下进行验证。该方法基于近期发现的Pitman-Yor过程的瓮模型表示，与现有最优方法相比具有更高精度，同时在计算效率和理论透明性方面实现显著提升。本研究建立了创新过程解释中的关键瓮模型与非参数贝叶斯推断之间的明确联系，为设计面向复杂相关模式与非平稳动态的高效推断方法开辟了新途径。