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Entropy and inference, revisited | | Ilya Nemenman
; Fariel Shafee
; William Bialek
; | | Date: |
15 Aug 2001 | | Journal: | Advances in Neural Information Processing Systems 14, 2002. MIT Press | | Subject: | Data Analysis, Statistics and Probability | physics.data-an | | Abstract: | 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. | | Source: | arXiv, physics/0108025 | | Services: | Forum | Review | PDF | Favorites | |
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