The permutation entropy rate equals the metric entropy rate for ergodic information sources and ergodic dynamical systems

被引:82
作者
Amigó, JM
Kennel, MB [1 ]
Kocarev, L
机构
[1] Univ Calif San Diego, Inst Nonlinear Sci, La Jolla, CA 92093 USA
[2] Univ Miguel Hernandez, Ctr Invest Operat, Elche 03202, Spain
关键词
entropy; permutation entropy; Kolmogorov-Sinai entropy; symbolic dynamics;
D O I
10.1016/j.physd.2005.07.006
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Permutation entropy quantifies the diversity of possible orderings of the values a random or deterministic system can take, as Shannon entropy quantifies the diversity of values. We show that the metric and permutation entropy rates-measures of new disorder per new observed value-are equal for ergodic finite-alphabet information sources (discrete-time stationary stochastic processes). With this result, we then prove that the same holds for deterministic dynamical systems defined by ergodic maps on n-dimensional intervals. This result generalizes a previous one for piecewise monotone interval maps on the real line [C. Bandt, G. Keller, B. Pompe, Entropy of interval maps via permutations, Nonlinearity 15 (2002) 1595-1602.] at the expense of requiring ergodicity and using a definition of permutation entropy rate differing modestly in the order of two limits. The case of non-ergodic finite-alphabet sources is also studied and an inequality developed. Finally, the equality of permutation and metric entropy rates is extended to ergodic non-discrete information sources when entropy is replaced by differential entropy in the usual way. (c) 2005 Elsevier B.V. All rights reserved.
引用
收藏
页码:77 / 95
页数:19
相关论文
共 15 条
[1]   Estimating the entropy rate of spike trains via Lempel-Ziv complexity [J].
Amigó, JM ;
Szczepanski, J ;
Wajnryb, E ;
Sanchez-Vives, MV .
NEURAL COMPUTATION, 2004, 16 (04) :717-736
[2]   Permutation entropy: A natural complexity measure for time series [J].
Bandt, C ;
Pompe, B .
PHYSICAL REVIEW LETTERS, 2002, 88 (17) :4
[3]   Entropy of interval maps via permutations [J].
Bandt, C ;
Keller, G ;
Pompe, B .
NONLINEARITY, 2002, 15 (05) :1595-1602
[4]   Statistically relaxing to generating partitions for observed time-series data [J].
Buhl, M ;
Kennel, MB .
PHYSICAL REVIEW E, 2005, 71 (04)
[5]  
Cover T. M., 2005, ELEM INF THEORY, DOI 10.1002/047174882X
[6]  
GRAY RM, 1990, ENTROPY INFORMATION
[7]  
KATOK A, 1998, INTRO THEORY DYNAMIC
[8]   Estimating entropy rates with Bayesian confidence intervals [J].
Kennel, MB ;
Shlens, J ;
Abarbanel, HDI ;
Chichilnisky, EJ .
NEURAL COMPUTATION, 2005, 17 (07) :1531-1576
[9]  
Kennel MB, 2002, PHYS REV E, V66, DOI [10.1103/PhysRevE.66.069903, 10.1103/PhysRevE.66.059903]
[10]   Nonparametric entropy estimation for stationary processes and random fields, with applications to English text [J].
Kontoyiannis, I ;
Algoet, PH ;
Suhov, YM ;
Wyner, AJ .
IEEE TRANSACTIONS ON INFORMATION THEORY, 1998, 44 (03) :1319-1327