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.

翻译：在本文中,我展示了如何利用神经网络从观测到的点模式中同时估计空间点过程模型中所有未知参数。该方法可以适用于任何可以从中模拟的点进程模型。通过模拟研究,我得出结论,该方法很好地回收了参数,在某些情况下提供了比最常用方法更好的估计。我还说明了该方法如何用于真正的数据示例。