Information flow within stochastic dynamical systems. Export

@article{citeulike:3424734, abstract = {Information flow or information transfer is an important concept in general physics and 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. An explicit formula has been obtained for the resulting transfer measure, which possesses a property of transfer asymmetry and, if the stochastic perturbation to the receiving component does not rely on the giving component, has a form the same as that for the corresponding deterministic system. This formula is further illustrated and validated with a two-dimensional Langevin equation. 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 flow analysis thus extends the traditional notion of correlation analysis and/or mutual information analysis by providing a quantitative measure of causality between dynamical events.}, author = {Liang, X. S.}, citeulike-article-id = {3424734}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/18850999}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=18850999}, issn = {1539-3755}, journal = {Physical review. E, Statistical, nonlinear, and soft matter physics}, keywords = {causality, dynamical\_system, fokker-planck, information, langevin, mathematical\_model, mutual\_information, physics, shannon\_entropy, stochastic}, month = {September}, number = {3 Pt 1}, posted-at = {2008-10-17 16:39:30}, priority = {2}, title = {Information flow within stochastic dynamical systems.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/18850999}, volume = {78}, year = {2008} }

TY - JOUR ID - citeulike:3424734 L3 - citeulike-article-id:3424734 N2 - Information flow or information transfer is an important concept in general physics and 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. An explicit formula has been obtained for the resulting transfer measure, which possesses a property of transfer asymmetry and, if the stochastic perturbation to the receiving component does not rely on the giving component, has a form the same as that for the corresponding deterministic system. This formula is further illustrated and validated with a two-dimensional Langevin equation. 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 flow analysis thus extends the traditional notion of correlation analysis and/or mutual information analysis by providing a quantitative measure of causality between dynamical events. IS - 3 Pt 1 JF - Physical review. E, Statistical, nonlinear, and soft matter physics SN - 1539-3755 TI - Information flow within stochastic dynamical systems. VL - 78 KW - causality KW - dynamical_system KW - fokker-planck KW - information KW - langevin KW - mathematical_model KW - mutual_information KW - physics KW - shannon_entropy KW - stochastic AU - Liang, XS PY - 2008/09// UR - http://view.ncbi.nlm.nih.gov/pubmed/18850999 ER -