http://nova.newcastle.edu.au/vital/access/services/Feed ${session.getAttribute("locale")} 5 http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:11039 A generalized Statistical Complexity Measure (SCM) is a functional that characterizes the probability distribution P associated to the time series generated by a given dynamical system. It quantifies not only randomness but also the presence of correlational structures. We review here several fundamental issues in such a respect, namely, (a) the selection of the information measure I; (b) the choice of the probability metric space and associated distance D; (c) the question of defining the so-called generalized disequilibrium Q; (d) the adequate way of picking up the probability distribution P associated to a dynamical system or time series under study, which is indeed a fundamental problem. In this communication we show (point d) that sensible improvements in the final results can be expected if the underlying probability distribution is “extracted” via appropriate consideration regarding causal effects in the system’s dynamics. 2012-07-03T03:18:43.252Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:6097 The generalized Statistical Complexity Measure (SCM) is a functional that characterizes the probability distribution P associated to the time series generated by a dynamical system under study. It quantifies not only randomness but also the presence of correlational structures. In this seminar several fundamental issues are reviewed: a) selection of the information measure I; b) selection of the probability metric space and its corresponding distance D; c) definition of the generalized disequilibrium Q; d) selection of the probability distribution P associated to a dynamical system or time series under study, which in fact! is a basic problem. Here we show that improvements can be expected if the underlying probability distribution is "extracted" by appropriate consideration regarding causal effects in the system's dynamics. Several well-known model-generated time series, usually regarded as being of either stochastic or chaotic nature, are analyzed. The main achievement of this approach is the possibility of clearly distinguish between them in the Entropy-Complexity representation space, something that is rather difficult otherwise. 2010-05-07T02:20:05.233Z ]]>