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Estimation of the information by an adaptive partitioning of the observation space
Abstract
We demonstrate that it is possible to approximate the mutual information arbitrarily closely in probability by calculating the relative frequencies on appropriate partitions and achieving conditional independence on the rectangles of which the partitions are made. Empirical results, including a comparison with maximum-likelihood estimators, are presented.
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Citations
Cited by
Kraskov, A., Stögbauer, H., & Grassberger, P. (2004) · Physical Review E · doi:10.1103/physreve.69.066138 · Cited by 3,512
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Reshef, D. N., Reshef, Y. A., Finucane, H. K., Grossman, S. R., McVean, G., Turnbaugh, P. J., Lander, E. S., Mitzenmacher, M., & Sabeti, P. C. (2011) · Science · doi:10.1126/science.1205438 · Cited by 3,339
Estimation of entropy and mutual information
Paninski, L. (2003) · Neural Computation · doi:10.1162/089976603321780272 · Cited by 1,442
Nonlinear multivariate analysis of neurophysiological signals
Pereda, E., Quiroga, R. Q., & Bhattacharya, J. (2005) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2005.10.003 · Cited by 1,076
Causality detection based on information-theoretic approaches in time series analysis
HLAVACKOVASCHINDLER, K., PALUS, M., VEJMELKA, M., & BHATTACHARYA, J. (2007) · Physics Reports · doi:10.1016/j.physrep.2006.12.004 · Cited by 800
Metadata
- Authors
- Darbellay, G. A., & Vajda, I.
- Venue
- IEEE Transactions on Information Theory
- Published
- 1999-05
- Volume
- 45
- Issue
- 4
- Pages
- 1315-1321
- ISSN
- 0018-9448 (ISSN-L)
- OpenAlex
- W2127234432
- Cited by
- 493
- References
- 0
- FWCI
- 4.48
- Type
- journal article
- Language
- en
- Open access
- closed
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- Darbellay, G. A., & Vajda, I. (1999). Estimation of the information by an adaptive partitioning of the observation space. IEEE Transactions on Information Theory. https://doi.org/10.1109/18.761290
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
