标题： 恐怖组织活动概况的隐马尔可夫模型 显示英文标题

标题： Hidden Markov models for the activity profile of terrorist groups

摘要： 本文工作的主要重点在于开发用于描述恐怖组织活动概况的模型，检测该概况中的突然上升和下降，并且总体上在一段时间内跟踪它。 为了实现这一目标，开发了一个包含$d$个状态的隐马尔可夫模型（HMM），该模型捕捉了描述该组织动态及其活动概况的潜在状态。 最简单的设置$d=2$对应于动态被粗略量化为“活跃”和“不活跃”的情况。 然后开发了一种利用底层 HMM 结构的状态估计策略，用于突发检测和跟踪。 事实证明，这种策略能够以学习底层模型为代价来跟踪即使是非持久的变化，这些变化仅持续很短的时间。 提供了来自开源数据库的真实恐怖主义数据案例研究，以展示所提出方法的性能。 显示英文摘要

摘要： The main focus of this work is on developing models for the activity profile of a terrorist group, detecting sudden spurts and downfalls in this profile, and, in general, tracking it over a period of time. Toward this goal, a $d$-state hidden Markov model (HMM) that captures the latent states underlying the dynamics of the group and thus its activity profile is developed. The simplest setting of $d=2$ corresponds to the case where the dynamics are coarsely quantized as Active and Inactive, respectively. A state estimation strategy that exploits the underlying HMM structure is then developed for spurt detection and tracking. This strategy is shown to track even nonpersistent changes that last only for a short duration at the cost of learning the underlying model. Case studies with real terrorism data from open-source databases are provided to illustrate the performance of the proposed methodology.