摘要
隐马尔可夫模型 ( HMM)是一个能够通过可观测的数据很好地捕捉真实空间统计性质的随机模型 ,该模型已成功地运用于语音识别 ,目前 HMM已开始应用于生物信息学 ( bioinformatics) ,已在生物序列分析中得到了广泛的应用 .本文首先介绍了 HMM的基本结构 ,然后着重讨论了 HMM在 DNA序列的多重比对 。
Hidden Markov Models(HMM) is a stochastic model that accurately captures the statistical properties of (observed) real world data. HMM is very well suited for many tasks in bioinformatics, although they have been successfully (applied) to speech recognition. This paper review the theory of HMM, and introduce the applications of HMM in biological sequence analysis, with a focus on the multiple alignment of DNA (sequence and genefinding.)
出处
《大学数学》
2004年第5期24-29,共6页
College Mathematics
