BART-based inference for Poisson processes

Stamatina Lamprinakou, Mauricio Barahona, Seth Flaxman, Sarah Filippi, Axel Gandy and Emma J. McCoy

Computational Statistics & Data Analysis, 2023, vol. 180, issue C

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.

Keywords: BART; Bayesian inference; Regression trees; Poisson processes; Nonparametric regression; Intensity estimation (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:180:y:2023:i:c:s0167947322002389

DOI: 10.1016/j.csda.2022.107658

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