BAYESIAN INFERENCE FOR PARTIAL ORDERS FROM RANDOM LINEAR EXTENSIONS: POWER RELATIONS FROM 12TH CENTURY ROYAL ACTA

• 成果类型: Article
• 署名作者: Nicholls, Geoff K.; Lee, Jeong Eun; Karn, Nicholas; Johnson, David; Huang, Rukuang; Muir-Watt, Alexis
• 署名单位: University of Oxford; University of Auckland; University of Southampton; University of Oxford; University of Oxford
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/24-AOAS2002
• 发表日期: 2025
• 页码: 1663-1690
• 关键词: mallows; models

摘要

In the eleventh and twelfth centuries in England, Wales and Normandy, royal acta were legal documents in which witnesses were listed in order of social status. Any bishops present were listed as a group. For our purposes each witness-list is an ordered permutation of bishop names with a known date or date-range. Changes over time in the order bishops are listed may reflect changes in their authority. Historians would like to detect and quantify these changes. There is no reason to assume that the underlying social order, which constrains bishop-order, within lists is a complete order. We therefore model the evolving social order as an evolving partial ordered set or poset. We construct a hidden Markov model for these data. The hidden state is an evolving poset (the evolving social hierarchy) and the emitted data are random total orders (dated lists) respecting the poset present at the time the order was observed. This generalises existing models for rank-order data such as Mallows and Plackett-Luce. We account for noise via a random queue-jumping process. Our latent-variable prior for the random process of posets is marginally consistent. A parameter controls poset depth, and actorcovariates inform the position of actors in the hierarchy. We fit the model, estimate posets and find evidence for changes in status over time. We interpret our results in terms of court politics. Simpler models, based on bucket orders and vertex-series-parallel orders, are rejected. We compare our results with a time-series extension of the Plackett-Luce model. Our software is publicly available.