This paper presents a model of recurrent multinomial sequences. Though there exists a quite considerable literature on modeling autocorrelation in numerical data and sequences of categorical outcomes, there is currently no systematic method of modeling patterns of recurrence in categorical sequences. This paper develops a means of discovering recurrent patterns by employing a more restrictive Markov assumption. The resulting model, which I call the recurrent multinomial model, provides a parsimonious representation of recurrent sequences, enabling the investigation of recurrences on longer time scales than existing models. The utility of recurrent multinomial models is demonstrated by applying them to the case of conversational turn-taking in meetings of the Federal Open Market Committee (FOMC). Analyses are effectively able to discover norms around turn-reclaiming, participation, and suppression and to evaluate how these norms vary throughout the course of the meeting.

本文提出了一个循环多项式序列的模型。尽管存在相当多的关于在数值数据和分类结果序列中建模自相关的文献，但目前没有系统的方法来建模分类序列中的复发模式。本文开发了一种通过采用更具限制性的马尔可夫假设来发现循环模式的方法。由此产生的模型，我称之为循环多项式模型，提供了循环序列的简约表示，与现有模型相比，能够在更长的时间尺度上研究循环。通过将循环多项式模型应用于联邦公开市场委员会 (FOMC) 会议中的对话轮换案例，证明了它们的实用性。