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Properties of the maximum a posteriori path estimator in hidden Markov models

机译:隐马尔可夫模型中最大后验路径估计量的性质

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A hidden Markov model (HMM) consists of a nonobservable Markov chain X=(X/sub 0/,X/sub 1/,...) and a measured process Y=(Y/sub 0/,Y/sub 1/,...) whose distribution is determined by X. To estimate the hidden path of X up to time n,X/sub 0/,X/sub 1/,...,X/sub n/, by the observations Y/sub 0/,Y/sub 1/,...,Y/sub n/, usually the maximum a posteriori probability path estimator (MAP path estimator) is applied. An effective means for calculating this estimator is the Viterbi algorithm, which is widely employed in the fields of coding theory, correction of intersymbol interference and text recognition. Here, properties of the MAP estimator are derived. Under a certain Condition C, it is shown that the limiting process U=(U/sub 0/,U/sub 1/,...) is a regenerative process. Particularly, this means that U has an asymptotic distribution, satisfies the Central Limit Theorem, and possesses a mean error. Furthermore, Condition C is satisfied for a broad class of HMMs including the most important case for applications, the HMM with additive white Gaussian noise.
机译:隐藏的马尔可夫模型(HMM)由不可观察的马尔可夫链X =(X / sub 0 /,X / sub 1 /,...)和测得的过程Y =(Y / sub 0 /,Y / sub 1 / ,...),其分布由X确定。要通过观测Y估算X直到时间n,X / sub 0 /,X / sub 1 /,...,X / sub n /的隐藏路径。 / sub 0 /,Y / sub 1 /,...,Y / sub n /,通常使用最大后验概率路径估计器(MAP路径估计器)。维特比算法是计算此估计量的有效方法,该算法广泛应用于编码理论,符号间干扰的校正和文本识别等领域。在此,导出MAP估计器的属性。在一定条件C下,表明极限过程U =(U / sub 0 /,U / sub 1 /,...)是再生过程。特别地,这意味着U具有渐近分布,满足中心极限定理,并且具有均值误差。此外,条件C适用于广泛的HMM类型,包括最重要的应用场合,即具有加性高斯白噪声的HMM。

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