Empirical and hierarchical Bayesian estimation of ancestral states
Empirical and hierarchical Bayesian estimation of ancestral states
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DOI：
10.1080/10635150119871
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发表时间：
2001-05-01
影响因子：
6.5
通讯作者：
Bollback, JP
中科院分区：
文献类型：
--
作者：
Huelsenbeck, JP;Bollback, JP
已经提出了几种方法来推断祖先节点上的状态。这些方法在估计祖先字符状态时假设特定的树和分支长度集。因此，对祖先状态的推断是以树和分支长度为真为条件的。我们开发了一个层次贝叶斯方法推断树的祖先状态。该方法利用马尔可夫链蒙特卡罗方法综合了树、分支长度和替代模型参数的不确定性。我们比较了祖先状态的层次贝叶斯推断与祖先状态的推断下，一个特定的树是正确的假设。我们发现，这些方法是相关的，但适应的系统发育模型的参数的不确定性可以使祖先状态的推断，甚至比他们将在经验贝叶斯分析更不确定。
Several methods have been proposed to infer the states at the ancestral nodes on a phylogeny. These methods assume a specific tree and set of branch lengths when estimating the ancestral character state. Inferences of the ancestral states, then, are conditioned on the tree and branch lengths being true. We develop a hierarchical Bayes method for inferring the ancestral states on a tree. The method integrates over uncertainty in the tree, branch lengths, and substitution model parameters by using Markov chain Monte Carlo. We compare the hierarchical Bayes inferences of ancestral states with inferences of ancestral states made under the assumption that a specific tree is correct. We find that the methods are correlated, but that accommodating uncertainty in parameters of the phylogenetic model can make inferences of ancestral states even more uncertain than they would be in an empirical Bayes analysis.