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• W2077048218 abstract "We characterize the complexity of geodesic paths on a curved statistical manifold M_{s} through the asymptotic computation of the information geometric complexity V_{M_{s}} and the Jacobi vector field intensity J_{M_{s}}. The manifold M_{s} is a 2l-dimensional Gaussian model reproduced by an appropriate embedding in a larger 4l-dimensional Gaussian manifold and endowed with a Fisher-Rao information metric g_{{mu}{nu}}({Theta}) with non-trivial off diagonal terms. These terms emerge due to the presence of a correlational structure (embedding constraints) among the statistical variables on the larger manifold and are characterized by macroscopic correlational coefficients r_{k}. First, we observe a power law decay of the information geometric complexity at a rate determined by the coefficients r_{k} and conclude that the non-trivial off diagonal terms lead to the emergence of an asymptotic information geometric compression of the explored macrostates {Theta} on M_{s}. Finally, we observe that the presence of such embedding constraints leads to an attenuation of the asymptotic exponential divergence of the Jacobi vector field intensity." @default.
• W2077048218 created "2016-06-24" @default.
• W2077048218 creator A5027136097 @default.
• W2077048218 creator A5056419860 @default.
• W2077048218 date "2011-03-01" @default.
• W2077048218 modified "2023-10-17" @default.
• W2077048218 title "Quantifying the complexity of geodesic paths on curved statistical manifolds through information geometric entropies and Jacobi fields" @default.
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• W2077048218 doi "https://doi.org/10.1016/j.physd.2010.11.013" @default.
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