Numerical Approximations of Log Gaussian Cox Process (Student Abstract)

AAAI 2022 pp. 12923-12924

doi:10.1609/AAAI.V36I11.21598 /aaai/2022/buetgolfouse2022aaai-numerical/

Abstract

This paper considers a multi-state Log Gaussian Cox Process (`"LGCP'') on a graph, where transmissions amongst states are calibrated using a non-parametric approach. We thus consider multi-output LGCPs and introduce numerical approximations to compute posterior distributions extremely quickly and in a completely transparent and reproducible fashion. The model is tested on historical data and shows very good performance.

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Cite

Text

Buet-Golfouse and Roggeman. "Numerical Approximations of Log Gaussian Cox Process (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21598

Markdown

[Buet-Golfouse and Roggeman. "Numerical Approximations of Log Gaussian Cox Process (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/buetgolfouse2022aaai-numerical/) doi:10.1609/AAAI.V36I11.21598

BibTeX

@inproceedings{buetgolfouse2022aaai-numerical,
  title     = {{Numerical Approximations of Log Gaussian Cox Process (Student Abstract)}},
  author    = {Buet-Golfouse, Francois and Roggeman, Hans},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {12923-12924},
  doi       = {10.1609/AAAI.V36I11.21598},
  url       = {https://mlanthology.org/aaai/2022/buetgolfouse2022aaai-numerical/}
}