Abstract
We discuss the fundamental theoretical framework together with numerous results obtained by the authors and colleagues over an extended period of investigation on the Information Geometric Approach to Chaos (IGAC).
Highlights
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The system is described by a statistical model specified in terms of probability distributions that are characterized by statistical macrovariables
If it is assumed that the system changes, the corresponding statistical model evolves from its initial to final configurations in a manner specified by Entropic Dynamics (ED, [6])
Summary
Statistical models are employed to formulate probabilistic descriptions of systems of arbitrary nature when only partial knowledge about the system is available. From the perspective of this hybrid framework, such complexity indicators can be understood as being quantitative measures that describe the complication of inferring macroscopic predictions about statistical models. Entropic methods are utilized to obtain an initial, static statistical model of the system In this way, the system is described by a statistical model specified in terms of probability distributions that are characterized by statistical macrovariables. The ED framework can be viewed as a form of constrained information dynamics that is formulated on statistical manifolds, the elements of which are probability distributions. Modeling strategies of this kind can only be corroborated a posteriori This fact implies that in the event inferred predictions fail to match experimental measurements, a new set of information constraints should be chosen. We introduce suitable indicators of complexity within the IGAC
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