Discussion on "Horseshoe-based Bayesian nonparametric estimation of effective population size trajectories" by James R. Faulkner, Andrew F. Magee, Beth Shapiro, and Vladimir N. Minin.
Discussion on "Horseshoe-based Bayesian nonparametric estimation of effective population size trajectories" by James R. Faulkner, Andrew F. Magee, Beth Shapiro, and Vladimir N. Minin.
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James R. Faulkner、Andrew F. Magee、Beth Shapiro 和 Vladimir N. Minin 对“有效人口规模轨迹的基于马蹄形贝叶斯非参数估计”的讨论。
DOI：
10.1111/biom.13275
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发表时间：
2020
期刊：
影响因子：
1.9
通讯作者：
Palacios,JuliaA
中科院分区：
文献类型：
--
作者：
Cappello,Lorenzo;Ghosh,Swarnadip;Palacios,JuliaA
作者提出了一个有吸引力的解决方案，长期存在的问题的局部自适应高斯过程先验的动态推理。虽然基于高斯过程的非线性动力学已经使用了超过10年（Minin等人，2008; Gill等人，二〇一三年; Palacios和Minin，2013年），这些方法先验地假设了一个控制整个种群规模历史的平滑度的单一精度参数，在平滑度随时间变化或突然变化的情况下限制了后验估计的精度。作者提出了一个马蹄形马尔可夫随机场（HSMRF）的顺序在一个规则的固定网格的时间点的对数有效人口规模轨迹的先验。𝑝HSMRF是灵活的局部自适应建模的每一个p阶前向差分的对数轨迹与先验尖峰在0与柯西样重尾。HSMRF支持小种群规模跳跃的小方差和大种群规模跳跃的大方差。我们讨论了所提出的方法的两个方面：（a）后验检查和模型选择，可以伴随HSMRF建模工具，和（B）的HSMRF模型的能力，以区分替代人口规模的轨迹，可以转化为有意义的科学发现。在这个讨论中，我们假设推理设置，其中系谱观察。
The authors present an attractive solution to a long-standing problem of local adaptivity of Gaussian process priors for phylodynamic inference. While Gaussian process–based phylodynamics have been used for over 10 years (Minin et al., 2008; Gill et al., 2013; Palacios and Minin, 2013), these methods a priori assume a single precision parameter that controls smoothness over the whole population size history, limiting precision of posterior estimates in cases of variable smoothness over time or abrupt changes. The authors propose a horseshoe Markov random field (HSMRF) prior of order 𝑝 on the log-effective population size trajectory at a regular fixed grid of 𝐻+ 1 time points. The HSMRF is flexible to local adaptivity modeling each pth-order forward difference of the logtrajectory with a prior that spikes at 0 with Cauchy-like heavy tails. The HSMRF favors small variance of small population size jumps and large variance of large population size jumps. We discuss two aspects of the proposed method:(a) posterior checks and model selection that can accompany HSMRF modeling tools, and (b) the ability of the HSMRF model to differentiate between alternative population size trajectories that can be translated into meaningful scientific discoveries. In this discussion, we assume the inference setting in which a genealogy is observed.