Institut de Physique Théorique
Direction de la Recherche Fondamentale  -  Saclay
UMR 3681 - INP
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Mercredi 22 novembre 2017

Publication : t17/144

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Improved Pseudolikelihood Regularization and Decimation methods on Non-linearly Interacting Systems with Continuous Variables

Marruzzo A. ()
Tyagi P. ()
Antenucci F. (CEA, IPhT (Institut de Physique Théorique), F-91191 Gif-sur-Yvette, France)
Pagnani A. ()
Leuzzi L. ()
Abstract:
We propose and test improvements to state-of-the-art techniques of Bayesian statistical inference based on pseudolikelihood maximization with ℓ 1 regularization and with decimation. In particular, we present a method to determine the best value of the regularizer parameter starting from a hypothesis testing technique. Concerning the decimation, we also analyze the worst case scenario's in which there is no sharp peak in the tilded-pseudolikelihood function, firstly defined as a criterion to stop the decimation. Techniques are applied to noisy systems with non-linear dynamics, mapped onto multi-variable interacting Hamiltonian effective models for waves and phasors. Results are analyzed varying the number of available samples and the externally tunable temperature-like parameter mimicking real data noise. Eventually the behavior of inference procedures described are tested against a wrong hypothesis: non-linearly generated data are analyzed with a pairwise interacting hypothesis. Our analysis shows that, looking at the behavior of the inverse graphical problem as data size increases, the methods exposed allow to rule out a wrong hypothesis.
Année de publication : 2017
Preprint : arXiv:1708.00787
Langue : Anglais

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