Gaussian Process Modulated Cox Processes under Linear Inequality Constraints

Understand

Gaussian process (GP) modulated Cox processes are widely used to model point patterns.

• Existing approaches require a mapping (link function) between the unconstrained GP and the positive intensity function.
• This commonly yields solutions that do not have a closed form or that are restricted to specific covariance functions.
• We introduce a novel finite approximation of GP-modulated Cox processes where positiveness conditions can be imposed directly on the GP, with no restrictions on the covariance function.