In:
SciPost Physics, Stichting SciPost, Vol. 5, No. 1 ( 2018-07-17)
Abstract:
We propose and test improvements to state-of-the-art techniques of
Bayeasian statistical inference based on pseudolikelihood maximization with \ell_1 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 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 mimicing 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.
Type of Medium:
Online Resource
ISSN:
2542-4653
DOI:
10.21468/SciPostPhys
DOI:
10.21468/SciPostPhys.5.1.002
Language:
Unknown
Publisher:
Stichting SciPost
Publication Date:
2018
detail.hit.zdb_id:
2886659-9