Entropy and inference, revisited
Data Analysis, Statistics and Probability
2007-05-23 v2
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.
Cite
@article{arxiv.physics/0108025,
title = {Entropy and inference, revisited},
author = {Ilya Nemenman and Fariel Shafee and William Bialek},
journal= {arXiv preprint arXiv:physics/0108025},
year = {2007}
}
Comments
LaTex2e, 9 pages, 5 figures; references added, minor revisions introduced, formatting errors corrected