期刊
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
卷 11, 期 3, 页码 678-689出版社
AMER STATISTICAL ASSOC
DOI: 10.1198/106186002402
关键词
conditional probability; EM algorithm; forward-backward; scaling; variance
This article describes a new algorithm for exact computation of the observed information matrix in hidden Markov models that may be performed in a single pass through the data. The score vector and log-likelihood are computed in the same pass. The new algorithm is derived from the forward-back ward algorithm traditionally used to evaluate the likelihood in hidden Markov models. Our result is discussed in the context of previous approaches that have been used to obtain approximate standard errors of parameter estimates in these models. Implications for parameter estimation are also discussed. An application of the proposed methods to rainfall occurrence data is provided.
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