系统发育学
选型
选择(遗传算法)
最大似然
贝叶斯概率
进化生物学
计算机科学
机器学习
人工智能
生物
统计
数学
遗传学
基因
作者
Jack Sullivan,Paul Joyce
标识
DOI:10.1146/annurev.ecolsys.36.102003.152633
摘要
▪ Abstract Investigation into model selection has a long history in the statistical literature. As model-based approaches begin dominating systematic biology, increased attention has focused on how models should be selected for distance-based, likelihood, and Bayesian phylogenetics. Here, we review issues that render model-based approaches necessary, briefly review nucleotide-based models that attempt to capture relevant features of evolutionary processes, and review methods that have been applied to model selection in phylogenetics: likelihood-ratio tests, AIC, BIC, and performance-based approaches.
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