BART-based inference for Poisson processes
Abstract
The effectiveness of Bayesian Additive Regression Trees (BART) has been demonstrated in a variety of contexts including non-parametric regression and classification. A BART scheme for estimating the intensity of inhomogeneous Poisson processes is introduced. Poisson intensity estimation is a vital task in various applications including medical imaging, astrophysics and network traffic analysis. The new approach enables full posterior inference of the intensity in a non-parametric regression setting. The performance of the novel scheme is demonstrated through simulation studies on synthetic and real datasets up to five dimensions, and the new scheme is compared with alternative approaches.
Cite
@article{arxiv.2005.07927,
title = {BART-based inference for Poisson processes},
author = {Stamatina Lamprinakou and Mauricio Barahona and Seth Flaxman and Sarah Filippi and Axel Gandy and Emma McCoy},
journal= {arXiv preprint arXiv:2005.07927},
year = {2022}
}
Comments
Accepted version including Supplementary Material. To appear in Computational Statistics & Data Analysis (CSDA)