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Strong law of large numbers for generalized sample relative entropy of non homogeneous Markov chains

机译:非齐次马尔可夫链的广义样本相对熵的强大数定律

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摘要

In this paper, we study the strong law of large numbers for the generalized sample relative entropy of non homogeneous Markov chains taking values from a finite state space. First, we introduce the definitions of generalized sample relative entropy and generalized sample relative entropy rate. Then, using a strong limit theorem for the delayed sums of the functions of two variables and a strong law of large numbers for non homogeneous Markov chains, we obtain the strong law of large numbers for the generalized sample relative entropy of non homogeneous Markov chains. As corollaries, we obtain some important results.
机译:在本文中,我们研究了从有限状态空间取值的非齐次马氏链的广义样本相对熵的强大数定律。首先,我们介绍广义样本相对熵和广义样本相对熵率的定义。然后,针对两个变量的函数的延迟和使用强极限定理,对于非齐次马尔可夫链使用强数定律,对于非齐次马尔可夫链的广义样本相对熵,我们获得了大数定律。作为推论,我们获得了一些重要的结果。

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