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Recently, Point Process Learning was introduced as a powerful approach to fitting Papangelou conditional intensity models to point pattern data. This cross-validation-based statistical theory was shown to significantly outperform the state-of-the-art in the context of kernel intensity estimation. In this paper, we further illustrate its potential by showing that it outperforms the state-of-the-art when fitting a hard-core Gibbs model.

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Point Process Learning: A Cross-Validation-Based Statistical Framework for Point Processes

  • 会议论文

  • 发表日期：

    2025年

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  • Julia Jansson,
  • Christophe A. N. Biscio,
  • Mehdi Moradi,
  • Ottmar Cronie

摘要

Recently, Point Process Learning was introduced as a powerful approach to fitting Papangelou conditional intensity models to point pattern data. This cross-validation-based statistical theory was shown to significantly outperform the state-of-the-art in the context of kernel intensity estimation. In this paper, we further illustrate its potential by showing that it outperforms the state-of-the-art when fitting a hard-core Gibbs model.