Entropy 2014, 16(6), 2944-2958; doi:10.3390/e16062944
Article

Information Geometric Complexity of a Trivariate Gaussian Statistical Model

1,2,* email, 3email and 1,2email
Received: 1 April 2014; in revised form: 21 May 2014 / Accepted: 22 May 2014 / Published: 26 May 2014
(This article belongs to the Special Issue Information Geometry)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: We evaluate the information geometric complexity of entropic motion on low-dimensional Gaussian statistical manifolds in order to quantify how difficult it is to make macroscopic predictions about systems in the presence of limited information. Specifically, we observe that the complexity of such entropic inferences not only depends on the amount of available pieces of information but also on the manner in which such pieces are correlated. Finally, we uncover that, for certain correlational structures, the impossibility of reaching the most favorable configuration from an entropic inference viewpoint seems to lead to an information geometric analog of the well-known frustration effect that occurs in statistical physics.
Keywords: probability theory; Riemannian geometry; complexity
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MDPI and ACS Style

Felice, D.; Cafaro, C.; Mancini, S. Information Geometric Complexity of a Trivariate Gaussian Statistical Model. Entropy 2014, 16, 2944-2958.

AMA Style

Felice D, Cafaro C, Mancini S. Information Geometric Complexity of a Trivariate Gaussian Statistical Model. Entropy. 2014; 16(6):2944-2958.

Chicago/Turabian Style

Felice, Domenico; Cafaro, Carlo; Mancini, Stefano. 2014. "Information Geometric Complexity of a Trivariate Gaussian Statistical Model." Entropy 16, no. 6: 2944-2958.

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