Implications of uniformly distributed, empirically informed priors for phylogeographical model selection: A reply to Hickerson et al.
Implications of uniformly distributed, empirically informed priors for phylogeographical model selection: A reply to Hickerson et al.
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DOI：
10.1111/evo.12523
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发表时间：
2014-12-01
期刊：
影响因子：
3.3
通讯作者：
Sukumaran, Jeet
中科院分区：
文献类型：
--
作者：
Oaks, Jamie R.;Linkem, Charles W.;Sukumaran, Jeet
确定一系列人口分裂事件同时发生可能是一个有说服力的论点，即一个共同的过程影响了人口。最近，奥克斯·埃塔尔。()评估了近似贝叶斯模型选择方法(MsBayes)估计分类群之间同时分化的这种模式的能力，Hickerson等人对此进行了评估。()回应。两篇论文都同意，该方法实现的主要推断对先前的假设非常敏感，并且当先前关于分歧时间的不确定性用均匀分布表示时，经常错误地支持跨分类群的共同分歧。然而，对于这一问题的最佳解释和解决方案，论文存在分歧。奥克斯埃塔。()指出，该方法的行为是由于均匀分布的先验在发散时间上的强权重导致具有更多发散时间参数的模型(假设1)的边际似然更小(从而后验概率更小)；他们提出了替代的先验概率分布来避免这种强加权后验。希克森·埃塔尔。()建议的数值逼近误差导致msBayes分析偏向于集群发散的模型，因为当使用关于发散时间的广泛一致先验时，该方法的拒绝算法无法在合理的计算限制内充分采样较丰富的模型的参数空间(假设2)。作为一种潜在的解决方案，他们提出了一种模型平均方法，该方法使用狭义的、经验知情的统一先验。在这里，我们使用模拟数据和经验数据的分析来证明Hickerson等人的方法。()并不能减轻该方法错误地支持高度聚集的分歧模型的倾向，而且在某种意义上是危险的，因为经验导出的统一先验经常排除在考虑分歧-时间参数的真值之外。我们的结果还表明，msBayes分析倾向于支持共享分歧模型的主要原因是假设1，而假设2是对这种偏见的不成立的解释。总体而言，这一系列论文表明，如果我们之前的假设在参数空间的不太可能的区域中赋予了太多权重，以至于准确的后验支持错误的进化历史模型，那么再多的计算也无法挽救我们的推断。幸运的是，正如贝叶斯模型选择的基本原理所预测的那样，更灵活的分布适应了关于参数的先验不确定性，而不会在参数空间的大范围内以低似然方式赋予过多的权重，从而增加了该方法的稳健性和检测发散中的时间变化的能力。
Establishing that a set of population-splitting events occurred at the same time can be a potentially persuasive argument that a common process affected the populations. Recently, Oaks etal. () assessed the ability of an approximate-Bayesian model-choice method (msBayes) to estimate such a pattern of simultaneous divergence across taxa, to which Hickerson etal. () responded. Both papers agree that the primary inference enabled by the method is very sensitive to prior assumptions and often erroneously supports shared divergences across taxa when prior uncertainty about divergence times is represented by a uniform distribution. However, the papers differ about the best explanation and solution for this problem. Oaks etal. () suggested the method's behavior was caused by the strong weight of uniformly distributed priors on divergence times leading to smaller marginal likelihoods (and thus smaller posterior probabilities) of models with more divergence-time parameters (Hypothesis 1); they proposed alternative prior probability distributions to avoid such strongly weighted posteriors. Hickerson etal. () suggested numerical-approximation error causes msBayes analyses to be biased toward models of clustered divergences because the method's rejection algorithm is unable to adequately sample the parameter space of richer models within reasonable computational limits when using broad uniform priors on divergence times (Hypothesis 2). As a potential solution, they proposed a model-averaging approach that uses narrow, empirically informed uniform priors. Here, we use analyses of simulated and empirical data to demonstrate that the approach of Hickerson etal. () does not mitigate the method's tendency to erroneously support models of highly clustered divergences, and is dangerous in the sense that the empirically derived uniform priors often exclude from consideration the true values of the divergence-time parameters. Our results also show that the tendency of msBayes analyses to support models of shared divergences is primarily due to Hypothesis 1, whereas Hypothesis 2 is an untenable explanation for the bias. Overall, this series of papers demonstrates that if our prior assumptions place too much weight in unlikely regions of parameter space such that the exact posterior supports the wrong model of evolutionary history, no amount of computation can rescue our inference. Fortunately, as predicted by fundamental principles of Bayesian model choice, more flexible distributions that accommodate prior uncertainty about parameters without placing excessive weight in vast regions of parameter space with low likelihood increase the method's robustness and power to detect temporal variation in divergences.