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Fast non-parametric Bayesian inference on infinite trees

Hutter, Marcus


Given i.i.d. data from an unknown distribution, we consider the problem of predicting future items. An adaptive way to estimate the probability density is to recursively subdivide the domain to an appropriate data-dependent granularity. A Bayesian would assign a data-independent prior probability to "subdivide", which leads to a prior over infinite(ly many) trees. We derive an...[Show more]

CollectionsANU Research Publications
Date published: 2005-01
Type: Conference paper


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