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Fast Non-Parametric Bayesian Inference on Infinite Trees

Hutter, Marcus

Description

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 exact, fast, and simple inference algorithm for such a prior, for the data evidence, the predictive distribution, the...[Show more]

CollectionsANU Research Publications
Date published: 2005
Type: Conference paper
URI: http://hdl.handle.net/1885/57947
Source: Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics (AISTATS 2005)

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