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.

中文翻译：

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使用神经网络估计空间点过程模型中的参数

在本文中，我展示了如何使用神经网络从观察到的点模式同时估计空间点过程模型中的所有未知参数。该方法可以应用于任何可以模拟的点过程模型。通过模拟研究，我得出结论，该方法可以很好地恢复参数，并且在某些情况下提供比最常用方法更好的估计。我还说明了如何在真实数据示例中使用该方法。