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Open AccessArticle

Information Geometric Complexity of a Trivariate Gaussian Statistical Model

School of Science and Technology, University of Camerino, I-62032 Camerino, Italy
INFN-Sezione di Perugia, Via A. Pascoli, I-06123 Perugia, Italy
Department of Mathematics, Clarkson University, Potsdam, 13699 NY, USA
Author to whom correspondence should be addressed.
Entropy 2014, 16(6), 2944-2958;
Received: 1 April 2014 / Revised: 21 May 2014 / Accepted: 22 May 2014 / Published: 26 May 2014
(This article belongs to the Special Issue Information Geometry)
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. View Full-Text
Keywords: probability theory; Riemannian geometry; complexity probability theory; Riemannian geometry; complexity
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Felice, D.; Cafaro, C.; Mancini, S. Information Geometric Complexity of a Trivariate Gaussian Statistical Model. Entropy 2014, 16, 2944-2958.

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