Computational Aspects of Optional P\'{o}lya Tree
Computation
2013-09-24 v1
Abstract
Optional P\'{o}lya Tree (OPT) is a flexible non-parametric Bayesian model for density estimation. Despite its merits, the computation for OPT inference is challenging. In this paper we present time complexity analysis for OPT inference and propose two algorithmic improvements. The first improvement, named Limited-Lookahead Optional P\'{o}lya Tree (LL-OPT), aims at greatly accelerate the computation for OPT inference. The second improvement modifies the output of OPT or LL-OPT and produces a continuous piecewise linear density estimate. We demonstrate the performance of these two improvements using simulations.
Keywords
Cite
@article{arxiv.1309.5489,
title = {Computational Aspects of Optional P\'{o}lya Tree},
author = {Hui Jiang and John C. Mu and Kun Yang and Chao Du and Luo Lu and Wing Hung Wong},
journal= {arXiv preprint arXiv:1309.5489},
year = {2013}
}