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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.
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