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• W3176645761 abstract "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." @default.
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• W3176645761 date "2021-11-01" @default.
• W3176645761 modified "2023-10-18" @default.
• W3176645761 title "Accounting for Missing Actors in Interaction Network Inference from Abundance Data" @default.
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• W3176645761 doi "https://doi.org/10.1111/rssc.12509" @default.
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