Approximate Bayesian Computation and Model Validation for Repulsive Spatial Point Processes

Understand

In many applications involving spatial point patterns, we find evidence of inhibition or repulsion.

• The most commonly used class of models for such settings are the Gibbs point processes.
• A recent alternative, at least to the statistical community, is the determinantal point process.
• Here, we examine model fitting and inference for both of these classes of processes in a Bayesian framework.