随机环境中马氏过程的强遍历性及收敛速度

The Strong Ergodic Principle and Rates of Convergence for Markov Processes with Random Environment

文章引进随机环境中马氏过程的模型,研究了该模型的马氏过程满足强遍历性的各种充要条件;证明了当随机转移函数P(θ*[s,s+t);x,A)属于G+时,强遍历性蕴含了收敛的指数速度;找出了最佳收敛速度;并得到了达到最佳收敛速度的条件.

The paper introduces the model of Markov processes in random environments, studies the necessary and sufficient conditions for strong ergodic principles under this model. Moreover, it proves the strong ergodic principle includes the rates of convergence when the random transition function belongs to G^＋. It also finds the best rate of convergence and gets the condition for reaching the best rate of convergence.