Title

Progressive binary partitioning for performance improvement in multivariate density estimation

Authors

Aref Majdara, Michigan Technological UniversityFollow
Saeid Nooshabadi, Michigan Technological UniversityFollow

Document Type

Conference Paper/Presentation

Publication Date

5-2019

Abstract

This paper presents an algorithm for efficient multivariate density estimation, using a blockized implementation of the Bayesian sequential partitioning algorithm. We also present a method for improving the performance of the blockized density estimation, by progressively updating the partitions. With progressive partitioning algorithm, each block uses the results from the previously processed blocks, and thus, as the simulation results show, it improves the performance of the blockized algorithm, both in terms of estimation accuracy and computation time.

Publisher's Statement

Publisher's version of record: https://dx.doi.org/10.1109/ISCAS.2019.8702548

Publication Title

2019 IEEE International Symposium on Circuits and Systems (ISCAS)

Recommended Citation

Majdara, A., & Nooshabadi, S. (2019). Progressive binary partitioning for performance improvement in multivariate density estimation. 2019 IEEE International Symposium on Circuits and Systems (ISCAS). http://doi.org/10.1109/ISCAS.2019.8702548
Retrieved from: https://digitalcommons.mtu.edu/ece_fp/55