Baxter Permutation Process

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.

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Cite

Text

Nakano et al. "Baxter Permutation Process." Neural Information Processing Systems, 2020.

Markdown

[Nakano et al. "Baxter Permutation Process." Neural Information Processing Systems, 2020.](https://mlanthology.org/neurips/2020/nakano2020neurips-baxter/)

BibTeX

@inproceedings{nakano2020neurips-baxter,
  title     = {{Baxter Permutation Process}},
  author    = {Nakano, Masahiro and Kimura, Akisato and Yamada, Takeshi and Ueda, Naonori},
  booktitle = {Neural Information Processing Systems},
  year      = {2020},
  url       = {https://mlanthology.org/neurips/2020/nakano2020neurips-baxter/}
}