caa a22 4500 46789163X CHVBK 20180406152751.0 cr unu---uuuuu 170328e20060901xx s 000 0 eng 10.1007/s10955-006-9131-x doi (NATIONALLICENCE)springer-10.1007/s10955-006-9131-x Whitening as a Tool for Estimating Mutual Information in Spatiotemporal Data Sets [Elektronische Daten] [Andreas Galka, Tohru Ozaki, Jorge Bayard, Okito Yamashita] We address the issue of inferring the connectivity structure of spatially extended dynamical systems by estimation of mutual information between pairs of sites. The well-known problems resulting from correlations within and between the time series are addressed by explicit temporal and spatial modelling steps which aim at approximately removing all spatial and temporal correlations, i.e. at whitening the data, such that it is replaced by spatiotemporal innovations; this approach provides a link to the maximum-likelihood method and, for appropriately chosen models, removes the problem of estimating probability distributions of unknown, possibly complicated shape. A parsimonious multivariate autoregressive model based on nearest-neighbour interactions is employed. Mutual information can be reinterpreted in the framework of dynamical model comparison (i.e. likelihood ratio testing), since it is shown to be equivalent to the difference of the log-likelihoods of coupled and uncoupled models for a pair of sites, and a parametric estimator of mutual information can be derived. We also discuss, within the framework of model comparison, the relationship between the coefficient of linear correlation and mutual information. The practical application of this methodology is demonstrated for simulated multivariate time series generated by a stochastic coupled-map lattice. The parsimonious modelling approach is compared to general multivariate autoregressive modelling and to Independent Component Analysis (ICA). Springer Science + Business Media, Inc., 2006 time series analysis nationallicence autoregressive modelling nationallicence innovations nationallicence mutual information nationallicence maximum likelihood nationallicence whitening nationallicence spatiotemporal modelling nationallicence independent component analysis nationallicence Galka Andreas Institute of Statistical Mathematics (ISM), Minami-Azabu 4-6-7, 106-8569, Tokyo, Japan aut Ozaki Tohru Institute of Statistical Mathematics (ISM), Minami-Azabu 4-6-7, 106-8569, Tokyo, Japan aut Bayard Jorge Cuban Neuroscience Center, Ave 25 No. 5202 esquina 158 Cubanacán, POB 6880, 6990, Ciudad Habana, Cuba aut Yamashita Okito ATR Computational Neuroscience Laboratories, Hikaridai 2-2-2, 619-0288, Kyoto, Japan aut Journal of Statistical Physics Kluwer Academic Publishers-Plenum Publishers; http://www.springer-ny.com 124/5(2006-09-01), 1275-1315 0022-4715 124:5<1275 2006 124 10955 https://doi.org/10.1007/s10955-006-9131-x text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10955-006-9131-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Galka Andreas Institute of Statistical Mathematics (ISM), Minami-Azabu 4-6-7, 106-8569, Tokyo, Japan aut NATIONALLICENCE 700 1- Ozaki Tohru Institute of Statistical Mathematics (ISM), Minami-Azabu 4-6-7, 106-8569, Tokyo, Japan aut NATIONALLICENCE 700 1- Bayard Jorge Cuban Neuroscience Center, Ave 25 No. 5202 esquina 158 Cubanacán, POB 6880, 6990, Ciudad Habana, Cuba aut NATIONALLICENCE 700 1- Yamashita Okito ATR Computational Neuroscience Laboratories, Hikaridai 2-2-2, 619-0288, Kyoto, Japan aut NATIONALLICENCE 773 0- Journal of Statistical Physics Kluwer Academic Publishers-Plenum Publishers; http://www.springer-ny.com 124/5(2006-09-01), 1275-1315 0022-4715 124:5<1275 2006 124 10955 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer