一,概述
二,马尔科夫链模型状态转移矩阵的性质
以上面的这个状态转移矩阵为例。假设我们当前股市的概率分布为:[0.3,0.4,0.3],即30%概率的牛市,40%概率的熊盘与30%的横盘。然后这个状态作为序列概率分布的初始状态t0t0,将其带入这个状态转移矩阵计算t1,t2,t3...t1,t2,t3...的状态。
代码:
import numpy as np
matrix = np.matrix([[0.9,0.075,0.025],[0.15,0.8,0.05],[0.25,0.25,0.5]], dtype=float)
vector1 = np.matrix([[0.3,0.4,0.3]], dtype=float)
for i in range(100):
vector1 = vector1*matrix
print("Current round:" , i+1)
print(vector1)
运行结果