  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.
