# Bayesian Nonparametric Space Partitions: A Survey

arXiv: https://arxiv.org/abs/2002.11394 · HTML: https://mlsanity.com/paper/2002.11394/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2020.

## Built on by (most cited first, 4 of 4)

- [FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification](https://mlsanity.com/paper/2404.15657/) (2024)
- [Hierarchical Infinite Relational Model](https://mlsanity.com/paper/2108.07208/) (2021)
- [A Framework for an Assessment of the Kernel-target Alignment in Tree Ensemble Kernel Learning](https://mlsanity.com/paper/2108.08752/) (2021)
- [Graph-Split Bayesian Causal Forest for Spatial Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2609.36046/) (2026)

## Abstract (arXiv)

Bayesian nonparametric space partition (BNSP) models provide a variety of strategies for partitioning a D-dimensional space into a set of blocks. In this way, the data points lie in the same block would share certain kinds of homogeneity. BNSP models can be applied to various areas, such as regression/classification trees, random feature construction, relational modeling, etc. In this survey, we investigate the current progress of BNSP research through the following three perspectives: models, which review various strategies for generating the partitions in the space and discuss their theoretical foundation `self-consistency'; applications, which cover the current mainstream usages of BNSP models and their potential future practises; and challenges, which identify the current unsolved problems and valuable future research topics. As there are no comprehensive reviews of BNSP literature before, we hope that this survey can induce further exploration and exploitation on this topic.