Online change detection of Markov chains with unknown post-change transition probabilities
Online change detection of Markov chains with unknown post-change transition probabilities
复制标题
具有未知的变化后转移概率的马尔可夫链的在线变化检测
DOI：
10.1080/03610926.2013.833243
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Jianbin Yu
中科院分区：
文献类型：
--
作者：
Jinguo Xian;D. Han;Jianbin Yu
摘要研究了累积和(CUSUM)停止规则在线检测时齐马尔可夫链中未知变点的性能。在变换后转移概率未知的情况下，我们提出了两种累积型检测方案。第一种方案基于变化后转移概率的最大似然估计。该方案受到运算量的限制，而另一种方案基于从先验已知区域选择的参考转移概率来减轻运算量。我们给出了平均延迟时间和平均虚警间隔时间的界，以说明所提出方案的有效性。仿真结果也验证了所提方案的可行性。
ABSTRACT In this paper, we investigate the performance of cumulative sum (CUSUM) stopping rules for the online detection of unknown change point in a time homogeneous Markov chain. Under the condition that the post-change transition probabilities are unknown, we proposed two CUSUM type schemes for the detection. The first scheme is based on the maximum likelihood estimates of the post-change transition probabilities. This scheme is limited by its computation burden, which is mitigated by another scheme based on the reference transition probabilities selected from a prior known region. We give the bounds of the mean delay time and the mean time between false alarms to illustrate the effectiveness of the proposed schemes. The results of the simulation also demonstrate the feasibility of the proposed schemes.