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可列非齐次隐Markov模型的强大数定律

Strong law of large numbers for countable nonhomogeneous hidden Markov models
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摘要 隐马尔科夫模型被广泛的应用于弱相依随机变量的建模,是研究神经生理学、发音过程和生物遗传等问题的有力工具.研究了可列非齐次隐Markov模型的若干性质,得到了这类模型的强大数定律,推广了有限非齐次马氏链的一类强大数定律. Hidden Markov models have been widely used for modeling sequences of weakly dependent random variables, with application in areas such as speech processing, neurophysiology and biology. In this paper, we study some properties of countable nonhomogeneous hidden Markov models, get the strong law of large numbers for those Markov models and extend a class of the strong law of large numbers for finite nonhomogeneous Markov chains.
作者 杨国庆 杨卫国
机构地区 江苏大学理学院
出处 《纯粹数学与应用数学》 CSCD 2014年第6期618-626,共9页 Pure and Applied Mathematics
基金 国家自然科学基金(11071104)
关键词 可列非齐次隐Markov模型 强大数定律 非齐次马尔科夫链 countable nonhomogeneous hidden Markov models strong law of large numbers nonhomogeneous Markov chains
分类号 O211.62 [理学—概率论与数理统计]
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参考文献12

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二级参考文献21

  • 1杨卫国,吴小太,王豹.一类隐马尔可夫模型的若干极限性质[J].江苏大学学报（自然科学版）,2006,27(5):467-470. 被引量：3
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纯粹数学与应用数学
2014年 第6期

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