Information flow within stochastic dynamical systems
| dc.creator | Liang, X. San | |
| dc.date | 2007-10-04 | |
| dc.date | 2007-10-05 | |
| dc.date.accessioned | 2026-07-07T08:34:04Z | |
| dc.date.available | 2026-07-07T08:34:04Z | |
| dc.description | Information flow or information transfer is an important concept in dynamical systems which has applications in a wide variety of scientific disciplines. In this study, we show that a rigorous formalism can be established in the context of a generic stochastic dynamical system. The resulting measure of of information transfer possesses a property of transfer asymmetry and, when the stochastic perturbation to the receiving component does not rely on the giving component, has a form same as that for the corresponding deterministic system. An application with a two-dimensional system is presented, and the resulting transfers are just as expected. A remarkable observation is that, for two highly correlated time series, there could be no information transfer from one certain series, say $x_2$, to the other ($x_1$). That is to say, the evolution of $x_1$ may have nothing to do with $x_2$, even though $x_1$ and $x_2$ are highly correlated. Information transfer analysis thus extends the traditional notion of correlation analysis by providing a quantitative measure of causality between time series. | |
| dc.description | Revised version, 8 papges, 1 figure | |
| dc.identifier | https://arxiv.org/abs/0710.0913 | |
| dc.identifier | http://arxiv.org/abs/0710.0913 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/139309 | |
| dc.subject | Chaotic Dynamics | |
| dc.title | Information flow within stochastic dynamical systems | |
| dc.type | text |