共查询到17条相似文献,搜索用时 79 毫秒
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双无限环境中马氏链的强大数定律 总被引:2,自引:0,他引:2
在随机环境中马氏链的研究领域 ,构造了一时齐的马氏双链 ,讨论了它的存在性及基本性质 ,最后利用马氏双链的性质 ,得到了双无限环境中马氏链的函数极限定律 ,并给出了该链的函数强大数定律成立的两个充分条件 相似文献
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通过构造适当的非负鞅,将Doob鞅收敛定理应用于几乎处处收敛的研究,给出了一类非齐次树上m重连续状态非齐次马氏链的若干强大数定律,推广了相关结果. 相似文献
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马氏环境中马氏链的强大数定律 总被引:9,自引:0,他引:9
讨论了具有离散参数的马氏环境中马氏链的强大数定律,并给出了加在链和过程样本函数上的充分条件.同时深入研究了Rθ-链,得到马氏环境中马氏链强大数定律成立的充分条件. 相似文献
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利用鞅收敛定理讨论马氏环境中马氏链的强收敛性,建立相应的强大数定律,使得已知的一系列结果为其特例. 相似文献
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陈平炎 《数学物理学报(A辑)》2005,25(3):386-392
该文把同分布的两两NQD列的Kolmogorov强大数定律推广到了在一类广泛的条件下的不同分布的情形, 为此而建立的Kolmogorov Chung型强大数定律本身也是有意义的.
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We find conditions on a sequence of random variables to satisfy the strong law of large numbers (SLLN) under a rearrangement. It turns out that these conditions are necessary and sufficient for the permutational SLLN (PSLLN). By PSLLN we mean that the SLLN holds under almost all simple permutations within blocks the lengths of which grow exponentially (Prokhorov blocks). In the case of orthogonal random variables it is shown that Kolmogorov's condition, that is known not to be sufficient for SLLN, is actually sufficient for PSLLN. It is also shown that PSLLN holds for sequences that are strictly stationary with finite first moments. In the case of weakly stationary sequences a Gaposhkin result implies that SLLN and PSLLN are equivalent. Finally we consider the case of general norming and generalization of the Nikishin theorem. The methods of proof uses on the one hand the idea of Prokhorov blocks and Garsia's construction of product measure on the space of simple permutations, and on the other hand, a maximal inequality for permutations. 相似文献
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研究了一类非齐次马氏链———渐近循环马氏链泛函的强大数定律,首先引出了渐近循环马氏链的概念,然后给出了若干引理.利用了渐近循环马氏链关于状态序偶出现频率的强大数定理给出并证明了关于渐近循环马氏链泛函的强大数定律,所得定理作为推论可得到已有的结果. 相似文献
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Limit theorems for functionals of classical (homogeneous) Markov renewal and semi-Markov processes have been known for a long time, since the pioneering work of Pyke Schaufele (Limit theorems for Markov renewal processes, Ann. Math. Statist., 35(4):1746–1764, 1964). Since then, these processes, as well as their time-inhomogeneous generalizations, have found many applications, for example, in finance and insurance. Unfortunately, no limit theorems have been obtained for functionals of inhomogeneous Markov renewal and semi-Markov processes as of today, to the best of the authors’ knowledge. In this article, we provide strong law of large numbers and central limit theorem results for such processes. In particular, we make an important connection of our results with the theory of ergodicity of inhomogeneous Markov chains. Finally, we provide an application to risk processes used in insurance by considering a inhomogeneous semi-Markov version of the well-known continuous-time Markov chain model, widely used in the literature. 相似文献