Effects of correlated variability on information entropies in nonextensive systems

TL;DR

This study analyzes how spatial correlations affect Tsallis entropy and Fisher information in nonextensive systems, revealing that correlations influence these measures differently and impact estimation accuracy.

Contribution

It provides an analytic calculation of entropy and Fisher information in nonextensive systems with correlations, highlighting their contrasting responses to correlation sign and magnitude.

Findings

Fisher information increases with positive correlation and decreases with negative correlation.

Tsallis entropy decreases as the absolute value of correlation increases, regardless of sign.

Negative correlation improves the accuracy of fluctuation estimates via the Cramér-Rao inequality.

Abstract

We have calculated the Tsallis entropy and Fisher information matrix (entropy) of spatially-correlated nonextensive systems, by using an analytic non-Gaussian distribution obtained by the maximum entropy method. Effects of the correlated variability on the Fisher information matrix are shown to be different from those on the Tsallis entropy. The Fisher information is increased (decreased) by a positive (negative) correlation, whereas the Tsallis entropy is decreased with increasing an absolute magnitude of the correlation independently of its sign. This fact arises from the difference in their characteristics. It implies from the Cram\'{e}r-Rao inequality that the accuracy of unbiased estimate of fluctuation is improved by the negative correlation. A critical comparison is made between the present study and previous ones employing the Gaussian approximation for the correlated variability due to multiplicative noise.