Baxter Permutation Process
Masahiro Nakano, Akisato Kimura, Takeshi Yamada, Naonori Ueda
Abstract
In this paper, a Bayesian nonparametric (BNP) model for Baxter permutations (BPs), termed BP process (BPP) is proposed and applied to relational data analysis. The BPs are a well-studied class of permutations, and it has been demonstrated that there is one-to-one correspondence between BPs and several interesting objects including floorplan partitioning (FP), which constitutes a subset of rectangular partitioning (RP). Accordingly, the BPP can be used as an FP model. We combine the BPP with a multi-dimensional extension of the stick-breaking process called the {\it block-breaking process} to fill the gap between FP and RP, and obtain a stochastic process on arbitrary RPs. Compared with conventional BNP models for arbitrary RPs, the proposed model is simpler and has a high affinity with Bayesian inference.
BibTeX
@inproceedings{NEURIPS2020_6271faad,
author = {Nakano, Masahiro and Kimura, Akisato and Yamada, Takeshi and Ueda, Naonori},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {8648--8659},
publisher = {Curran Associates, Inc.},
title = {Baxter Permutation Process},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/6271faadeedd7626d661856b7a004e27-Paper.pdf},
volume = {33},
year = {2020}
}