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October 8, 2025Open Access

Nonparametric inference for nonstationary spatial point processes

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Authors

INIzabel NolauFGFlávio B. GonçalvesUniversidade Federal de Minas GeraisDGDani GamermanUniversidade Federal do Rio de Janeiro

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Overview

This paper proposes a Cox process model to capture nonstationarity in spatial point processes, implying enhanced inference accuracy.

Key Points

  • The proposed model captures complex spatial structures by allowing for nonstationarity and abrupt changes in intensity.
  • An MCMC algorithm enables exact inference, targeting the infinite-dimensional posterior distribution without approximation.
  • The methodology considers spatial covariates to address structured variations in intensity within the spatial domain.
  • Evaluation through synthetic examples demonstrates the model's flexibility and effectiveness in real-world applications.

Cite This Study

Nolau et al. (2025) studied this question.

synapsesocial.com/papers/68e6a0f4718ef0a556b33cd3https://doi.org/10.48550/arxiv.2507.17600
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