均匀分布的经验先验对地理模型选择的影响：对Hickerson等人的答复
Implications of uniformly distributed, empirically informed priors for phylogeographical model selection: A reply to Hickerson et al
摘要：
确定一组种群分裂事件同时发生，可能是一个有说服力的论点，即一个共同的过程影响了种群。Oaks et al.（2013）评估了近似贝叶斯方法（msBayes）估计分类群间同时分歧模式的能力，Hickerson et al.（2014）对此做出了回应。这两篇论文都认为该方法对先前的假设很敏感，并且经常错误地支持共享的分歧;论文的解释和解决方案不同。Oaks等人（2013）认为该方法的行为是由发散时间上的均匀先验的强权重引起的，导致具有更多发散时间参数的模型的边际似然较小（假设1）;他们提出了替代先验以避免强加权后验。Hickerson et al.（2014）建议数值近似误差导致msBayes分析偏向于聚类分歧模型（假设2）;他们建议使用狭窄的经验一致先验。在这里，我们证明了Hickerson等人的方法。（2014）并没有减轻该方法错误地支持聚类分歧模型的倾向，并且经常排除真实的参数值。我们的研究结果还表明，msBayes分析支持共享分歧模型的趋势主要是由于假设1。这一系列的论文表明，如果我们的先验假设在参数空间的不太可能的区域中放置了太多的权重，使得精确的后验支持错误的进化历史模型，那么无论多少计算都无法挽救我们的推理。幸运的是，更灵活的分布，适应有关参数的先验不确定性，而不放置过多的权重在参数空间的巨大区域与低可能性增加了该方法的鲁棒性和功率，以检测时间变化的分歧。
英文摘要：
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. Oaks et al. (2013) assessed the ability of an approximate-Bayesian method (msBayes) to estimate such a pattern of simultaneous divergence across taxa, to which Hickerson et al. (2014) responded. Both papers agree the method is sensitive to prior assumptions and often erroneously supports shared divergences; the papers differ about the explanation and solution. Oaks et al. (2013) suggested the method's behavior is caused by the strong weight of uniform priors on divergence times leading to smaller marginal likelihoods of models with more divergence-time parameters (Hypothesis 1); they proposed alternative priors to avoid strongly weighted posteriors. Hickerson et al. (2014) suggested numerical approximation error causes msBayes analyses to be biased toward models of clustered divergences (Hypothesis 2); they proposed using narrow, empirical uniform priors. Here, we demonstrate that the approach of Hickerson et al. (2014) does not mitigate the method's tendency to erroneously support models of clustered divergences, and often excludes the true parameter values. Our results also show that the tendency of msBayes analyses to support models of shared divergences is primarily due to Hypothesis 1. This series of papers demonstrate 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, 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
