An improved estimator of Shannon entropy with applications to systems with memory
Statistical Mechanics
2022-11-21 v2 Data Analysis, Statistics and Probability
Abstract
We investigate the memory properties of discrete sequences built upon a finite number of states. We find that the block entropy can reliably determine the memory for systems modeled as Markov chains of arbitrary finite order. Further, we provide an entropy estimator that remarkably gives accurate results when correlations are present. To illustrate our findings, we calculate the memory of daily precipitation series at different locations. Our results are in agreement with existing methods being at the same time valid in the undersampled regime and independent of model selection.
Keywords
Cite
@article{arxiv.2205.11931,
title = {An improved estimator of Shannon entropy with applications to systems with memory},
author = {Juan De Gregorio and David Sanchez and Raul Toral},
journal= {arXiv preprint arXiv:2205.11931},
year = {2022}
}