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September 30, 2025Open Access

Gaussian Process Methods for Covariate-Based Intensity Estimation

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Authors

PDPatric DolmetaUniversity of TurinMGMatteo GiordanoUniversity of Turin

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Overview

Nonparametric Bayesian inference improves intensity estimation in point processes, suggesting optimal methods for covariate data.

Key Points

  • Gaussian priors combined with flexible link functions yield minimax optimal posterior contraction rates, enhancing accuracy.
  • The approach effectively addresses intensity function estimation for point processes with stationary covariates, optimizing statistical procedures.
  • This work extends existing literature, particularly through popular methods like Matérn processes and common link functions.
  • Optimal procedures derived from this study may improve analysis in spatial statistics, particularly for covariate-driven data.

Cite This Study

Dolmeta et al. (2025) studied this question.

synapsesocial.com/papers/68dc12c58a7d58c25ebb0abchttps://doi.org/10.48550/arxiv.2505.20157
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