Accounting for Missing Actors in Interaction Network Inference from Abundance Data
针对计数数据中未观测变量可能导致的虚假条件依赖问题，提出一种混合泊松对数正态分布与树形图模型的方法，通过变分EM算法恢复包含缺失参与者的依赖结构，并在生态数据集上验证了其恢复环境驱动因素的能力。
Abstract Network inference aims at unravelling the dependency structure relating jointly observed variables. Graphical models provide a general framework to distinguish between marginal and conditional dependency. Unobserved variables (missing actors) may induce apparent conditional dependencies. In the context of count data, we introduce a mixture of Poisson log-normal distributions with tree-shaped graphical models, to recover the dependency structure, including missing actors. We design a variational EM algorithm and assess its performance on synthetic data. We demonstrate the ability of our approach to recover environmental drivers on two ecological data sets. The corresponding R package is available from github.com/Rmomal/nestor.