An Autoregressive Approach to Nonparametric Hierarchical Dependent Modeling
Abstract
We propose a conditional autoregression framework for a collection of random probability measures. Under this framework, we devise a conditional autoregressive Dirichlet process (DP) that we call one-parameter dependent DP (wDDP). The appealing properties of this specification are that it has two equivalent representations and its inference can be implemented in a conditional Polya urn scheme. Moreover, these two representations bear a resemblance to the Polya urn scheme and the stick-breaking representation in the conventional DP. We apply this wDDP to Bayesian multivariate-response regression problems. An efficient Markov chain Monte Carlo algorithm is developed for Bayesian computation and prediction.
Cite
Text
Zhang et al. "An Autoregressive Approach to Nonparametric Hierarchical Dependent Modeling." Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.Markdown
[Zhang et al. "An Autoregressive Approach to Nonparametric Hierarchical Dependent Modeling." Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.](https://mlanthology.org/aistats/2012/zhang2012aistats-autoregressive/)BibTeX
@inproceedings{zhang2012aistats-autoregressive,
title = {{An Autoregressive Approach to Nonparametric Hierarchical Dependent Modeling}},
author = {Zhang, Zhihua and Wang, Dakan and Chang, Edward},
booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics},
year = {2012},
pages = {1416-1424},
volume = {22},
url = {https://mlanthology.org/aistats/2012/zhang2012aistats-autoregressive/}
}