  Network inference aims at unraveling 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 datasets. The corresponding R package
is available from github.com/Rmomal/nestor.
