Fast Non-Parametric Bayesian Inference on Infinite Trees
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]
|Collections||ANU Research Publications|
|Source:||Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics (AISTATS 2005)|
|01_Hutter_Fast_Non-Parametric_Bayesian_2005.pdf||5.14 MB||Adobe PDF||Request a copy|
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