Imperial College London
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DrSarahFilippi

Faculty of Natural Sciences, Department of Mathematics

Reader in Statistical Machine Learning
 
 
 
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Contact

 

+44 (0)20 7594 8562s.filippi

 
 
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Location

 

523Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Lamprinakou:2023:10.1016/j.csda.2022.107658,
author = {Lamprinakou, S and Barahona, M and Flaxman, S and Filippi, S and Gandy, A and McCoy, EJ},
doi = {10.1016/j.csda.2022.107658},
journal = {Computational Statistics and Data Analysis},
pages = {1--25},
title = {BART-based inference for Poisson processes},
url = {http://dx.doi.org/10.1016/j.csda.2022.107658},
volume = {180},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Lamprinakou,S
AU  - Barahona,M
AU  - Flaxman,S
AU  - Filippi,S
AU  - Gandy,A
AU  - McCoy,EJ
DO  - 10.1016/j.csda.2022.107658
EP  - 25
PY  - 2023///
SN  - 0167-9473
SP  - 1
TI  - BART-based inference for Poisson processes
T2  - Computational Statistics and Data Analysis
UR  - http://dx.doi.org/10.1016/j.csda.2022.107658
UR  - http://hdl.handle.net/10044/1/105963
VL  - 180
ER  -
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