Infinite Mixtures of Trees
Abstract
Finite mixtures of tree-structured distributions have been shown to be efficient and effective in modeling multivariate distributions. Using Dirichlet processes, we extend this approach to allow countably many treestructured mixture components. The resulting Bayesian framework allows us to deal with the problem of selecting the number of mixture components by computing the posterior distribution over the number of components and integrating out the components by Bayesian model averaging. We apply the proposed framework to identify the number and the properties of predominant precipitation patterns in historical archives of climate data.
Cite
Text
Kirshner and Smyth. "Infinite Mixtures of Trees." International Conference on Machine Learning, 2007. doi:10.1145/1273496.1273549Markdown
[Kirshner and Smyth. "Infinite Mixtures of Trees." International Conference on Machine Learning, 2007.](https://mlanthology.org/icml/2007/kirshner2007icml-infinite/) doi:10.1145/1273496.1273549BibTeX
@inproceedings{kirshner2007icml-infinite,
title = {{Infinite Mixtures of Trees}},
author = {Kirshner, Sergey and Smyth, Padhraic},
booktitle = {International Conference on Machine Learning},
year = {2007},
pages = {417-423},
doi = {10.1145/1273496.1273549},
url = {https://mlanthology.org/icml/2007/kirshner2007icml-infinite/}
}