MAXIMUM-LIKELIHOOD-ESTIMATION FOR HIDDEN MARKOV-MODELS

STOCHASTIC PROCESSES AND THEIR APPLICATIONS

卷:40

Article

关键词: MARKOV CHAIN;    CONSISTENCY;    SUBADDITIVE ERGODIC THEOREM;    IDENTIFIABILITY;    ENTROPY;    KULLBACK-LEIBLER DIVERGENCE;    SHANNON-MCMILLAN-BREIMAN THEOREM;   

DOI  :  10.1016/0304-4149(92)90141-C

来源: Elsevier

【 摘 要 】

Hidden Markov models assume a sequence of random variables to be conditionally independent given a sequence of state variables which forms a Markov chain. Maximum-likelihood estimation for these models can be performed using the EM algorithm. In this paper the consistency of a sequence of maximum-likelihood estimators is proved. Also, the conclusion of the Shannon-McMillan-Breiman theorem on entropy convergence is established for hidden Markov models.

【 授权许可】

Free