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Network Inference of Dynamic Models by the Combination of Spanning Arborescences

Abstract : In this paper, we tackle the problem of generative learning of dynamic models from ”fat” time series data (high #variables/#individuals ratio), leading to a high sensitivity of learned models to the dataset noise. To overcome this problem, we propose a method computing a mixture of many highly biased but optimal spanning arborescences obtained from many perturbed versions of the original dataset, introducing variance to counterbalance the strong arborescence bias. The method is theoretically at the boundary between structure oriented Bayesian model averaging and recent work on density estimation using mixtures of poly-trees through a perturb and combine framework, transposed to a dynamic setting. In practice, preliminary results on the recent DREAM D8C1 challenge are promising.
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Contributor : Anthony Coutant <>
Submitted on : Friday, August 2, 2019 - 5:40:14 PM
Last modification on : Saturday, February 15, 2020 - 2:03:17 AM
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  • HAL Id : hal-02262577, version 1



Anthony Coutant, Céline Rouveirol. Network Inference of Dynamic Models by the Combination of Spanning Arborescences. Journées Ouvertes en Biologie, Informatique et Mathématiques, Jul 2017, Lille, France. ⟨hal-02262577⟩



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