Parametric Identification of Stochastic Interaction Networks

Abstract : This paper aims at solving the problem of parameter identification in interaction networks. Purely discontinuous Markov processes are the most appropriate choice for modeling such systems, and it is of paramount importance to estimate the unknown characteristics of the model given the measured data. The model induces a Fokker-Planck-Kolmogorov equation along with moment equations, and achieving parametric identification based on direct solutions of these equations has remained elusive. We propose a novel approach which utilizes applied stochastic analysis for continuous-time Markov processes to lift the curse of dimensionality in parametric identification. We illustrate through two case studies of biological network modeling.
Type de document :
Communication dans un congrès
International Joint Conference on Neural Networks (IJCNN), May 2017, Anchorage, Alaska, United States. 〈10.1109/ijcnn.2017.7966137 〉
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https://hal-centralesupelec.archives-ouvertes.fr/hal-01526271
Contributeur : Hana Baili <>
Soumis le : lundi 22 mai 2017 - 20:54:30
Dernière modification le : jeudi 5 avril 2018 - 12:30:05

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Hana Baili. Parametric Identification of Stochastic Interaction Networks. International Joint Conference on Neural Networks (IJCNN), May 2017, Anchorage, Alaska, United States. 〈10.1109/ijcnn.2017.7966137 〉. 〈hal-01526271〉

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