Titlebook: Evolutionary Genomics; Statistical and Comp Maria Anisimova Book‘‘‘‘‘‘‘‘ 2019Latest edition The Editor(s) (if applicable) and The Author(s)

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Probability, Statistics, and Computational Sciencetions. 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 provide the basis for models that are discussed in more detail in subsequent chapters.

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The Methodology Behind Network Thinking: Graphs to Analyze Microbial Complexity and Evolutionsity of users. This chapter introduces the different kinds of networks based on sequence similarity that are already available to tackle a wide range of biological questions, including sequence similarity networks, gene-sharing networks and bipartite graphs, and a guide for their construction and analyses.

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Modern Phylogenomics: Building Phylogenetic Trees Using the Multispecies Coalescent Modelt from the species tree, is one such region where many so-called “coalescent” methods are inconsistent. Second-generation implementations of the MSC employed Bayesian or likelihood models; these are consistent in all regions of gene tree space, but Bayesian methods in particular are incapable of han

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Bayesian Molecular Clock Dating Using Genome-Scale Datasetsut the analysis. We pay particular attention to how to set up the analysis and the priors and how to diagnose the MCMC algorithm used to obtain the posterior estimates of divergence times and evolutionary rates.