RECOMMENDATIONS FOR USING MSBAYES TO INCORPORATE UNCERTAINTY IN SELECTING AN ABC MODEL PRIOR: A RESPONSE TO OAKS ET AL
RECOMMENDATIONS FOR USING MSBAYES TO INCORPORATE UNCERTAINTY IN SELECTING AN ABC MODEL PRIOR: A RESPONSE TO OAKS ET AL
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DOI：
10.1111/evo.12241
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发表时间：
2014-01-01
期刊：
影响因子：
3.3
通讯作者：
Takebayashi, Naoki
中科院分区：
文献类型：
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作者：
Hickerson, Michael J.;Stone, Graham N.;Takebayashi, Naoki
先验规范是近似贝叶斯计算（ABC）中参数估计和模型比较的重要组成部分。奥克斯等人。提出了基于模拟的 msBayes 功效分析，并得出结论，msBayes 检测类群中真正随机发散时间的功效较低，并表明原因是 Lindley 悖论。尽管预测相似，但我们表明，他们的发现从根本上可以通过先验采样不足来解释，先验采样不足是由于选择不当的广泛先验而导致的，这些先验严重地对高可能性的非同时发散历史进行了欠采样。在对菲律宾岛脊椎动物数据的重新分析中，我们展示了如何通过扩展先前开发的程序来规避这个问题，该程序使用贝叶斯模型平均来适应先前选择的不确定性。当使用这些过程时，msBayes 支持最近的分歧，而不支持 Oaks 等人中的同步分歧。数据，我们进一步提出了一个模拟分析，证明 msBayes 可以在发散时间较窄的先验条件下具有较高的能力来检测异步发散。我们的研究结果强调需要探索高维 ABC 采样器的合理参数空间和先验采样效率。我们讨论了 msBayes 的潜在改进，并得出结论：当正确地与模型平均一起使用时，msBayes 仍然是一个有效且强大的工具。
Prior specification is an essential component of parameter estimation and model comparison in Approximate Bayesian computation (ABC). Oaks etal. present a simulation-based power analysis of msBayes and conclude that msBayes has low power to detect genuinely random divergence times across taxa, and suggest the cause is Lindley's paradox. Although the predictions are similar, we show that their findings are more fundamentally explained by insufficient prior sampling that arises with poorly chosen wide priors that critically undersample nonsimultaneous divergence histories of high likelihood. In a reanalysis of their data on Philippine Island vertebrates, we show how this problem can be circumvented by expanding upon a previously developed procedure that accommodates uncertainty in prior selection using Bayesian model averaging. When these procedures are used, msBayes supports recent divergences without support for synchronous divergence in the Oaks etal. data and we further present a simulation analysis that demonstrates that msBayes can have high power to detect asynchronous divergence under narrower priors for divergence time. Our findings highlight the need for exploration of plausible parameter space and prior sampling efficiency for ABC samplers in high dimensions. We discus potential improvements to msBayes and conclude that when used appropriately with model averaging, msBayes remains an effective and powerful tool.