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Entropy and Inference, Revisited
Open accessAbstract
We study properties of popular near-uniform (Dirichlet) priors for learning undersampled probability distributions on discrete nonmetric spaces and show that they lead to disastrous results. However, an Occam-style phase space argument expands the priors into their infinite mixture and resolves most of the observed problems. This leads to a surprisingly good estimator of entropies of discrete distributions.
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Cited by
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Kinney, J. B., & Atwal, G. S. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1309933111 · Cited by 667
Correcting for the sampling bias problem in spike train information measures
Panzeri, S., Senatore, R., Montemurro, M. A., & Petersen, R. S. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00559.2007 · Cited by 378
Zoppoli, P., Morganella, S., & Ceccarelli, M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-154 · Cited by 248
Metadata
- Authors
- Nemenman, I., Shafee, F., & Bialek, W.
- Venue
- Advances in Neural Information Processing Systems 14
- Publisher
- The MIT Press
- Published
- 2002-11-08
- Pages
- 471-478
- OpenAlex
- W2097173970
- Cited by
- 211
- References
- 0
- FWCI
- 7.10
- Type
- book chapter
- Language
- en
- Open access
- green
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