Fast Non-Parametric Bayesian Inference on Infinite Trees
Date
2005
Authors
Hutter, Marcus
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Publisher
Society for Artificial Intelligence and Statistics
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, and other quantities.
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Keywords
Keywords: Inference algorithm; Infinite trees; Non-parametric Bayesian; Predictive distributions; Prior probability; Probability densities; Artificial intelligence; Bayesian networks; Forestry; Inference engines; Probability density function; Trees (mathematics)
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Source
Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics (AISTATS 2005)
Type
Conference paper
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2037-12-31
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