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首页> 外文期刊>Journal of applied statistics >Bayesian model averaging for estimating the number of classes: applications to the total number of species in metagenomics
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Bayesian model averaging for estimating the number of classes: applications to the total number of species in metagenomics

机译：贝叶斯模型平均估计类别数：在宏基因组学中应用于物种总数

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摘要

The species abundance distribution and the total number of species are fundamental descriptors of the biodiversity of an ecological community. This paper focuses on situations where large numbers of rare species are not observed in the data set due to insufficient sampling of the community, as is the case in metagenomics for the study of microbial diversity. We use a truncated mixture model for the observations to explicitly tackle the missing data and propose methods to estimate the total number of species and, in particular, a Bayesian credibility interval for this number. We focus on computationally efficient procedures with variational methods and importance sampling as opposed to Markov Chain Monte Carlo sampling, and we use Bayesian model averaging as the number of components of the mixture model is unknown.
机译：物种丰富度分布和物种总数是生态群落生物多样性的基本描述。本文关注的是由于缺乏足够的社区采样而在数据集中未观察到大量稀有物种的情况，例如用于研究微生物多样性的宏基因组学就是这种情况。我们使用截断的混合模型进行观测，以明确处理缺失的数据，并提出方法来估计物种总数，尤其是估计该数量的贝叶斯可信区间。与马尔可夫链蒙特卡洛采样相反，我们着重于采用变分方法和重要性采样的高效计算程序，并且由于混合模型的组件数未知，因此我们使用贝叶斯模型平均。

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