本文对BMS进行了介绍，并分别利用随机过程中马尔可夫链的知识和时间序列中的INAR(1)模型对BMS进行了研究。
This paper will first introduce BMS, then study BMS by Markov chain in stochastic process and INAR (1) model in time series.
对间断雨量序列有雨、无雨状态的交替变化规律，本文沿用马尔可夫转移概率描述。
The Markov transition probability has been used to describe the interchange properties of the states of rain and non-rain in this paper.
而且，我们还提出一种所谓更新图，它和马尔可夫图的关系类似于更新序列和马尔可夫链的关系。
Moreover, we also find the relation between Markov digraphs and renewal digraphs, similar to that between Markov chains and renewal sequences.
一般情况下对平稳遍历马尔可夫链部分和序列最小值分布进行精确尾估计是较困难的。
Generally, it is not easy to find the exact tail-estimation for the distribution of the minimum value in partial sum sequence of a stationary ergodic Markov chain.
马尔可夫切换模型是一种研究时间序列结构性变化的方法。
Markov-switching model is a method applied to investigating the structural changes of time series.
最大熵马尔可夫通过改变概率转移函数，使得状态的转移与输入值以及前一状态相联系，很好地体现了序列的上下文信息。
MEMM change the probability function of the transition, so the current state is related to its previous state, and the context information is represented.
利用HRRP对雷达视角敏感这一特点，用隐马尔可夫过程表征多视角雷达回波序列，获得目标距离-方位两维信息。
HMM is used to describe multi-azimuth radar profiles to obtain the distance-azimuth information of radar target through that radar HRRP is sensitive to radar azimuth.
利用HRRP对雷达视角敏感这一特点，用隐马尔可夫过程表征多视角雷达回波序列，获得目标距离-方位两维信息。
HMM is used to describe multi-azimuth radar profiles to obtain the distance-azimuth information of radar target through that radar HRRP is sensitive to radar azimuth.
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