Complex statistical modelling for phylogenetic inference

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Canadian Journal of Statistics-Revue Canadienne De Statistique Pub Date : 2022-10-29 DOI:10.1002/cjs.11741
Edward Susko
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Abstract

Molecular sequence data are a primary source of information about evolutionary relationships. Over the past few decades, there have been dramatic increases in the sizes of data available. Consequently, focus has shifted towards increasingly complex models that are less prone to the biases that are a consequence of model misspecification. At the same time, the computational challenges, which were always substantial, have become greater due to these increasingly complex models and larger data sizes. In this article, we review phylogenetic inference using sequence data and some recent advances in phylogenetic modelling. We discuss strategies for dealing with complex models, future challenges and paths forward.

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系统发育推断的复杂统计模型
分子序列数据是进化关系信息的主要来源。在过去的几十年里,可用的数据量急剧增加。因此,焦点已经转移到越来越复杂的模型上,这些模型不太容易产生由模型错误说明导致的偏差。与此同时,由于这些日益复杂的模型和更大的数据规模,计算挑战变得越来越大,这一直是实质性的。在这篇文章中,我们回顾了利用序列数据的系统发育推断和系统发育模型的一些最新进展。我们讨论了处理复杂模型的策略、未来的挑战和前进的道路。
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
62
审稿时长
>12 weeks
期刊介绍: The Canadian Journal of Statistics is the official journal of the Statistical Society of Canada. It has a reputation internationally as an excellent journal. The editorial board is comprised of statistical scientists with applied, computational, methodological, theoretical and probabilistic interests. Their role is to ensure that the journal continues to provide an international forum for the discipline of Statistics. The journal seeks papers making broad points of interest to many readers, whereas papers making important points of more specific interest are better placed in more specialized journals. The levels of innovation and impact are key in the evaluation of submitted manuscripts.
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