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In this paper, we propose a doubly stochastic spatial point process modelwith both aggregation and repulsion. This model combines the ideas behindStrauss processes and log Gaussian Cox processes. The likelihood for this modelis not expressible in closed form but it is easy to simulate realisations underthe model. We therefore explain how to use approximate Bayesian computation(ABC) to carry out statistical inference for this model. We suggest a methodfor model validation based on posterior predictions and global envelopes. Weillustrate the ABC procedure and model validation approach using both simulatedpoint patterns and a real data example.

Approximate Bayesian inference for a spatial point process model exhibiting regularity and random aggregation