Using neural networks to estimate parameters in spatial point process models

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 pattern. The method applied any which it is possible simulate from. Through simulation study, conclude that the recovers well and some situations provide better estimates than most commonly methods. also illustrate on real data example.