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