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Entropy estimation of symbol sequences
Open accessAbstract
We discuss algorithms for estimating the Shannon entropy h of finite symbol sequences with long range correlations. In particular, we consider algorithms which estimate h from the code lengths produced by some compression algorithm. Our interest is in describing their convergence with sequence length, assuming no limits for the space and time complexities of the compression algorithms. A scaling law is proposed for extrapolation from finite sample lengths. This is applied to sequences of dynamical systems in non-trivial chaotic regimes, a 1-D cellular automaton, and to written English texts.
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Citations
Cites
On the Length of Programs for Computing Finite Binary Sequences
Chaitin, G. J. (1966) · Journal of the ACM · doi:10.1145/321356.321363 · Cited by 913
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Crutchfield, J. P., & Feldman, D. P. (2003) · Chaos: An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.1530990 · Cited by 368
Heart rate variability in bipolar mania and schizophrenia
Henry, B. L., Minassian, A., Paulus, M. P., Geyer, M. A., & Perry, W. (2010) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2009.07.011 · Cited by 197
Authorship Attribution for Neural Text Generation
Uchendu, A., Le, T., Shu, K., & Lee, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.673 · Cited by 120
Metadata
- Authors
- Schürmann, T., & Grassberger, P.
- Venue
- Chaos: An Interdisciplinary Journal of Nonlinear Science
- Publisher
- AIP Publishing
- Published
- 1996-09-01
- Volume
- 6
- Issue
- 3
- Pages
- 414-427
- ISSN
- 1054-1500 (ISSN-L)
- 1089-7682
- OpenAlex
- W2018891628
- Cited by
- 319
- References
- 1
- FWCI
- 1.92
- Type
- journal article
- Language
- en
- Open access
- green
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- Schürmann, T., & Grassberger, P. (1996). Entropy estimation of symbol sequences. Chaos: An Interdisciplinary Journal of Nonlinear Science. https://doi.org/10.1063/1.166191
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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