标题： 多分辨率Kingman-Tajima共祖计数的顺序重要性抽样 显示英文标题

标题： Sequential importance sampling for multi-resolution Kingman-Tajima coalescent counting

摘要： 从分子序列数据中进行进化参数的统计推断依赖于共祖模型，以考虑样本的共享系谱祖先。 然而，推断算法无法扩展到现有的数据集。 提高计算效率的一种策略是依赖于更简单的共祖和突变模型，从而产生更小的隐藏状态空间。 对不同分辨率下系谱树状态空间基数的估计对于决定给定数据集的最佳建模策略至关重要。 据我们所知，目前既没有精确的方法也没有近似方法来确定这些基数。 我们提出了一种顺序重要性抽样算法，以估计在不同共祖分辨率下的系谱树空间的基数。 我们的抽样方案依次处理数据所施加的组合约束集。 我们通过模拟分析不同系谱树空间的基数，研究有利于使用更粗粒度分辨率的情况。 我们从1000基因组项目中的线粒体DNA数据和美拉尼西亚人口的一个样本中估计系谱树空间的基数，以说明在哪些情况下采用更粗粒度分辨率是有利的。 显示英文摘要

摘要： Statistical inference of evolutionary parameters from molecular sequence data relies on coalescent models to account for the shared genealogical ancestry of the samples. However, inferential algorithms do not scale to available data sets. A strategy to improve computational efficiency is to rely on simpler coalescent and mutation models, resulting in smaller hidden state spaces. An estimate of the cardinality of the state-space of genealogical trees at different resolutions is essential to decide the best modeling strategy for a given dataset. To our knowledge, there is neither an exact nor approximate method to determine these cardinalities. We propose a sequential importance sampling algorithm to estimate the cardinality of the space of genealogical trees under different coalescent resolutions. Our sampling scheme proceeds sequentially across the set of combinatorial constraints imposed by the data. We analyse the cardinality of different genealogical tree spaces on simulations to study the settings that favor coarser resolutions. We estimate the cardinality of genealogical tree spaces from mtDNA data from the 1000 genomes and a sample from a Melanesian population to illustrate the settings in which it is advantageous to employ coarser resolutions.