Exact Non-Parametric Bayesian Inference on Infinite Trees

Marcus Hutter

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arXiv · 0903.5342

Exact Non-Parametric Bayesian Inference on Infinite Trees

Marcus Hutter

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Abstract

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 effective model dimension, moments, and other quantities. We prove asymptotic convergence and consistency results, and illustrate the behavior of our model on some prototypical functions.

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BibTeX RIS

Marcus Hutter. 2009-03-30. Exact Non-Parametric Bayesian Inference on Infinite Trees. https://arxiv.org/abs/0903.5342

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