Entropy and inference, revisited

Details Entropy and inference, revisited Entropy and inference, revisited

2001-08-15

arxiv.org/abs/physics/0108025v2

Advances in Neural Information Processing Systems 14, 2002. MIT Press

Physics

Data Analysis, Statistics and Probability

LaTex2e, 9 pages, 5 figures; references added, minor revisions introduced, formatting errors corrected

Scientific paper

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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