Using neural networks to estimate parameters in spatial point process models

2021/09/30 by Ninna Vihrs, Vihrs, Ninna · 1 citation
Engineering · Mathematics · #3D Shape Modeling and Analysis #Algorithm #Artificial intelligence #Artificial neural network #Computer science #Data mining #FOS: Computer and information sciences #Mathematics #Methodology (stat.ME) #Point (geometry) #Point process #Point processes and geometric inequalities #Process (computing) #Statistics #stat.ME

paper · pdf · doi:10.48550/arxiv.2109.15056

published in arXiv (Cornell University) (Cornell University) · 23 pages, 19 figures, R code is attached as ancillary files

openalex publication_date 2021/09/30 · arxiv created 2022/04/13 · arxiv updated 2022/04/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08

Abstract

In this paper, I show how neural networks can be used to simultaneously estimate all unknown parameters in a spatial point process model from an observed point pattern. The method can be applied to any point process model which it is possible to simulate from. Through a simulation study, I conclude that the method recovers parameters well and in some situations provide better estimates than the most commonly used methods. I also illustrate how the method can be used on a real data example.

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