Beerenwinkel Niko, Siebourg Juliane
Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
Methods Mol Biol. 2012;855:77-110. doi: 10.1007/978-1-61779-582-4_3.
In this chapter, we review basic concepts from probability theory and computational statistics that are fundamental to evolutionary genomics. We provide a very basic introduction to statistical modeling and discuss general principles, including maximum likelihood and Bayesian inference. Markov chains, hidden Markov models, and Bayesian network models are introduced in more detail as they occur frequently and in many variations in genomics applications. In particular, we discuss efficient inference algorithms and methods for learning these models from partially observed data. Several simple examples are given throughout the text, some of which point to models that are discussed in more detail in subsequent chapters.
在本章中，我们回顾概率论和计算统计学的基本概念，这些概念是进化基因组学的基础。我们对统计建模进行了非常基础的介绍，并讨论了一般原则，包括最大似然法和贝叶斯推理。马尔可夫链、隐马尔可夫模型和贝叶斯网络模型会更详细地介绍，因为它们在基因组学应用中频繁出现且有多种变体。特别是，我们讨论了从部分观测数据学习这些模型的有效推理算法和方法。文中给出了几个简单的例子，其中一些指向后续章节中会更详细讨论的模型。
