Fast Non-Parametric Bayesian Inference on Infinite Trees
<|reference_start|>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 effective model dimension, and other quantities.<|reference_end|>
@article{hutter2004fast,
    title={Fast Non-Parametric Bayesian Inference on Infinite Trees},
    author={Marcus Hutter},
    journal={Proc. 10th International Conf. on Artificial Intelligence and
  Statistics (AISTATS-2005) 144-151},
    year={2004},
    number={IDSIA-24-04},
    archivePrefix={arXiv},
    eprint={math/0411515},
    primaryClass={math.ST cs.LG math.PR stat.TH}
}