Information Geometric Complexity of a Trivariate Gaussian Statistical Model

Entropy

Domenico Felice²  Carlo Cafaro¹ 

[1] Department of Mathematics, Clarkson University, Potsdam, 13699 NY, USA; E-Mail:;School of Science and Technology, University of Camerino, I-62032 Camerino, Italy; E-Mail:

关键词: probability theory;    Riemannian geometry;    complexity;   

DOI  :  10.3390/e16062944

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland