A crucial challenge for solving problems in conflict research is in leveraging the semi-supervised nature of the data that arise. Observed response data such as counts of battle deaths over time indicate latent processes of interest such as intensity and duration of conflicts, but defining and labeling instances of these unobserved processes requires nuance and imprecision. The availability of such labels, however, would make it possible to study the effect of intervention-related predictors -- such as ceasefires -- directly on conflict dynamics (e.g., latent intensity) rather than through an intermediate proxy like observed counts of battle deaths. Motivated by this problem and the new availability of the ETH-PRIO Civil Conflict Ceasefires data set, we propose a Bayesian autoregressive (AR) hidden Markov model (HMM) framework as a sufficiently flexible machine learning approach for semi-supervised regime labeling with uncertainty quantification. We motivate our approach by illustrating the way it can be used to study the role that ceasefires play in shaping conflict dynamics. This ceasefires data set is the first systematic and globally comprehensive data on ceasefires, and our work is the first to analyze this new data and to explore the effect of ceasefires on conflict dynamics in a comprehensive and cross-country manner.

翻译：解决冲突研究中出现的问题的关键挑战是利用数据半监督性质,即利用所出现数据的半监督性质。观察到的反应数据,如战时死亡数字,表明冲突强度和持续时间等潜在关注进程,但界定和标注这些未观察过程的事例需要细微和不精确。但是,这种标签的提供将使得有可能研究与干预有关的预测因素 -- -- 例如停火 -- -- 直接对冲突动态(例如潜在强度)的影响,而不是通过类似观察到的战斗死亡人数等中间代用数据。受这一问题和埃塞俄比亚-巴勒斯坦-巴勒斯坦-巴勒斯坦-巴勒斯坦-巴勒斯坦-巴勒斯坦冲突停火新数据集的驱动,我们提议巴耶西亚-巴勒斯坦-巴勒斯坦-巴勒斯坦-巴勒斯坦-内战停火模式框架(AR)隐藏的马尔科夫模式(HMMM)框架,作为半监督制度标记不确定性量化的足够灵活的机械学习方法。我们通过说明停火在冲突动态中发挥的作用,从而激励我们的方法。这一数据集是第一个关于停火的系统和全球综合数据,我们的工作是以全面的方式分析停火动态。