This paper will first introduce BMS, then study BMS by Markov chain in stochastic process and INAR (1) model in time series.
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。
In this paper, we research that the induced series of this model is geometrically ergodicity Markov chain and this model is adjoint geometrically ergodic.
在这篇文章中，讨论了由这个模型确定的导出序列的遍历性以及该模型的伴随几何遍历性。
The series are calculated and analyzed by correlation analysis, stochastic simulation, Monte Carlo, and Markov chain, Monte Carlo, so a group model of risk function is established.
采取相关分析、随机模拟、蒙特卡罗和马尔柯夫链等方法进行了一系列的分析和计算，从而建立了一组风险函数。
The series are calculated and analyzed by correlation analysis, stochastic simulation, Monte Carlo, and Markov chain, Monte Carlo, so a group model of risk function is established.
采取相关分析、随机模拟、蒙特卡罗和马尔柯夫链等方法进行了一系列的分析和计算，从而建立了一组风险函数。
应用推荐