ergodic markov chain meaning in Chinese
遍历马尔可夫链
Examples
- ( 2 ) stochastic theory and other correlative theories are used to analyze iga , and the immune extend population sequence formed by iga is proved to be an aperiodic irreducible ergodic markov chain . next , the global convergence of iga is proved
2 、利用随机过程理论及相关理论对免疫遗传算法进行分析，证明了由免疫遗传摘要算法形成的扩展免疫种群序列的强马尔可夫性，同时还进行了不可约性、非周期性、遍历性等性质的研究。 - In this dissertation , we firstly prove that any dirichlet problem is indeed equal to a voltages problem of networks . we give five solutions to dirichlet problem in two dimensions ; among these five solutions , we prove that the iteration solution and the solution of relaxations are exponential convergence , then we estimate their respective convergence rates ; secondly , we discuss random walks on general networks , prove that there is an one to one correspondence between networks and reversible ergodic markov chains ; thirdly , we give probabilistic interpretation of voltages for general networks : when a unit voltage is applied between a and b , making va = 1 and vb = 0 , the voltage vx at any point x represents the probability that a walker starting from x will return to a before reaching b ; furthermore , we study the relationship between effective resistance and escape probability : starting at a , the probability that the walk reaches b before returning to a is the ratio of the effective conductance and the total conductance
本文证明了任何边值的dirichlet问题都可转化为求解电路电压的问题：给出了计算平面格点上dirichlet问题的5种方法：证明了迭代法和松驰法都是指数收敛的，并分别给出收敛速度的估计；讨论了一般电路上的随机徘徊，验证了电路与可逆的遍历markov链是一一对应的；给出了电路电压的概率解释：当把1伏电压加于a ， b两端，使得v _ a = 1 ， v _ b = 0时，则x点的电压v _ x表示对应的markov链中，从x出发，到达b之前到达a的概率；进一步地，给出了逃离概率与有效电阻之间的关系：从a出发，在到达b之前到达a的概率为有效传导率与通过a的总传导率之比。 - In the research of the algorithms and theory of temporal difference learning , a new class of multi - step learning prediction algorithms based on linear function approximators and recursive least squares methods is proposed , which are called the rls - td ( t ) learning algorithm . the convergence with probability one of the rls - td ( t ) algorithm is proved for ergodic markov chains , and the conditions for convergence are analyzed
在时域差值学习( temporaldifferencelearning )学习算法和理论方面，首次提出了一种基于线性值函数逼近的多步递推最小二乘td ( ) ( rls - td ( ) )学习算法，并分析和证明了该算法在求解遍历markov链学习预测问题中的收敛条件和一致收敛性。