Vijayabaskar M S
School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK.
Methods Mol Biol. 2017;1552:1-12. doi: 10.1007/978-1-4939-6753-7_1.
A number of real-world systems have common underlying patterns among them and deducing these patterns is important for us in order to understand the environment around us. These patterns in some instances are apparent upon observation while in many others especially those found in nature are well hidden. Moreover, the inherent stochasticity in these systems introduces sufficient noise that we need models capable to handling it in order to decipher the underlying pattern. Hidden Markov model (HMM) is a probabilistic model that is frequently used for studying the hidden patterns in an observed sequence or sets of observed sequences. Since its conception in the late 1960s it has been extensively applied in biology to capture patterns in various disciplines ranging from small DNA and protein molecules, their structure and architecture that forms the basis of life to multicellular levels such as movement analysis in humans. This chapter aims at a gentle introduction to the theory of HMM, the statistical problems usually associated with HMMs and their uses in biology.
许多现实世界的系统之间存在共同的潜在模式，推断这些模式对我们理解周围环境很重要。这些模式在某些情况下观察时很明显，而在许多其他情况下，尤其是在自然界中发现的那些模式则隐藏得很好。此外，这些系统中固有的随机性引入了足够的噪声，我们需要能够处理它的模型来破译潜在模式。隐马尔可夫模型（HMM）是一种概率模型，常用于研究观察序列或观察序列集中的隐藏模式。自20世纪60年代末提出以来，它已在生物学中广泛应用，以捕捉从构成生命基础的小DNA和蛋白质分子、它们的结构和架构到多细胞水平（如人类运动分析）等各个学科的模式。本章旨在对HMM理论、通常与HMM相关的统计问题及其在生物学中的应用进行简要介绍。
