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

Abstract : We propose and test improvements to state-of-the-art techniques of Bayesian statistical inference based on pseudolikelihood maximization with $\ell_1$ 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.
Liste complète des métadonnées

https://hal-cea.archives-ouvertes.fr/cea-01586170
Contributeur : Emmanuelle De Laborderie <>
Soumis le : mardi 12 septembre 2017 - 15:19:59
Dernière modification le : vendredi 23 février 2018 - 09:28:02

Lien texte intégral

Identifiants

  • HAL Id : cea-01586170, version 1
  • ARXIV : 1708.00787

Citation

Alessia Marruzzo, Payal Tyagi, Fabrizio Antenucci, Andrea Pagnani, Luca Leuzzi. Improved Pseudolikelihood Regularization and Decimation methods on Non-linearly Interacting Systems with Continuous Variables. t17/144. 40 pages, 24 figures. 2017. 〈cea-01586170〉

Partager

Métriques

Consultations de la notice

32