Genealogical Working Distributions for Bayesian Model Testing with Phylogenetic Uncertainty
Genealogical Working Distributions for Bayesian Model Testing with Phylogenetic Uncertainty
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DOI：
10.1093/sysbio/syv083
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发表时间：
2016-03-01
影响因子：
6.5
通讯作者：
Suchard, Marc A.
中科院分区：
文献类型：
--
作者：
Baele, Guy;Lemey, Philippe;Suchard, Marc A.
使用贝叶斯因子比较模型的边缘似然估计经常伴随贝叶斯系统发育推断。在过去十年中，估计边际可能性的方法受到了越来越多的关注。特别是，路径抽样（PS）和垫脚石抽样（SS）引入贝叶斯遗传学模型选择的准确性大大提高。这些抽样技术现在被用来评估复杂的进化和种群遗传模型的经验数据集，但相当大的计算需求阻碍了他们的广泛采用。此外，当非常分散，但适当的先验指定的模型参数，数值问题复杂的探索的先验，一个必要的步骤，在边际似然估计使用PS或SS。为了避免这种不稳定性，最近提出了广义SS（GSS），引入了“工作分布”的概念，以促进或缩短集成过程的基础边际似然估计。然而，需要修复的树拓扑结构，目前限制了GSS在合并为基础的框架。在这里，我们通过放松固定的底层树拓扑假设来扩展GSS。为了这个目的，我们引入了一个“工作”的分布空间的系谱，这使得估计边缘的可能性，同时适应系统发育的不确定性。我们提出了两种不同的“工作”分布，帮助GSS优于PS和SS的准确性比较时，人口和进化模型应用于合成数据和现实世界的例子。此外，我们表明，使用非常分散的先验可以导致一个相当大的高估边际可能性时，使用PS和SS，同时仍然使用GSS方法检索正确的边际可能性。本文中使用的方法可以在BEAST中使用，BEAST是一个功能强大的用户友好的软件包，用于执行贝叶斯进化分析。
Marginal likelihood estimates to compare models using Bayes factors frequently accompany Bayesian phylogenetic inference. Approaches to estimate marginal likelihoods have garnered increased attention over the past decade. In particular, the introduction of path sampling (PS) and stepping-stone sampling (SS) into Bayesian phylogenetics has tremendously improved the accuracy of model selection. These sampling techniques are now used to evaluate complex evolutionary and population genetic models on empirical data sets, but considerable computational demands hamper their widespread adoption. Further, when very diffuse, but proper priors are specified for model parameters, numerical issues complicate the exploration of the priors, a necessary step in marginal likelihood estimation using PS or SS. To avoid such instabilities, generalized SS (GSS) has recently been proposed, introducing the concept of "working distributions" to facilitate-or shorten-the integration process that underlies marginal likelihood estimation. However, the need to fix the tree topology currently limits GSS in a coalescent-based framework. Here, we extend GSS by relaxing the fixed underlying tree topology assumption. To this purpose, we introduce a "working" distribution on the space of genealogies, which enables estimating marginal likelihoods while accommodating phylogenetic uncertainty. We propose two different "working" distributions that help GSS to outperform PS and SS in terms of accuracy when comparing demographic and evolutionary models applied to synthetic data and real-world examples. Further, we show that the use of very diffuse priors can lead to a considerable overestimation in marginal likelihood when using PS and SS, while still retrieving the correct marginal likelihood using both GSS approaches. The methods used in this article are available in BEAST, a powerful user friendly software package to perform Bayesian evolutionary analyses.