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一类广义隐马尔科夫模型的建模与参数估计(英文) 被引量:1

Modeling and Parameter Estimation of a Class of General Hidden Markov Model
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摘要 隐马尔科夫模型在很多方面已有广泛应用 .讨论了一类更为一般的模型 ,这类模型由WojciechPieczynski首次提出 ,并且给出了在图像识别中的应用 .这里首次给出在离散观测和离散状态下该模型的精确数学描述 ,其中包括建模、状态估计和参数估计 。 It is well-known that HMM has been widely used in many fields. In this paper we will discuss a more general model, which is similar to Pairwise Markov Model(PM M) proposed by Wojciech Pieczynski. Compared to HMM, the state process here is n ot necessarily a Markov chain. So it has more general applications in image segm entation, speech signal processing, and etc. We will give a complete mathematica l description for this model with discrete states and discrete observations, inc luding modeling, state estimation and parameter estimation, which haven't been s tudied before. Based on the method proposed here, we will get a recursive algori thm for the estimation of the state and the parameters.
作者 胡可 张大力
出处 《中国科学院研究生院学报》 CAS CSCD 2005年第2期210-217,共8页 Journal of the Graduate School of the Chinese Academy of Sciences
关键词 测度变换 递归参数估计 递归状态估计 广义隐马尔科夫模型 change of measure, recursive parameter estimation, recu rsive state estimation, General Hidden Markov Model (GHMM)
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参考文献4

  • 1L R Rabiner. A tutorial on Hidden Markov Models and selected applications in speech recognition. Proceedings of the IEEE, 1989,77(2) :257~286.
  • 2Wojciech Pieczynski. Pairwise Markov chains . IEEE Transanctions on Pattern Analysis and Machine Intelligence, 2003,25(5):634~639.
  • 3Robert J Elliott, L Aggoun, J B Moore. Hidden Markov Model estimation and control. New York:Springer-Verlag, 1995.
  • 4Francois Desbouvries, Wojciech Picczynski. Particle filtering with pairwise Markov processes, IEEE. 2003.

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