Determinantal thinning of point processes with network learning applications
This work addresses the need for tractable repulsive network models in statistical learning, offering an alternative to computationally intensive Gibbs point processes.
The authors introduced determinantal thinning, a new dependent thinning method for point processes in continuous space that produces repulsive network models, and demonstrated its application by imitating Matérn II hard-core thinning and a soft-core thinning based on nearest-neighbor triangles, leading to statistically optimized probabilistic transmission scheduling schemes.
A new type of dependent thinning for point processes in continuous space is proposed, which leverages the advantages of determinantal point processes defined on finite spaces and, as such, is particularly amenable to statistical, numerical, and simulation techniques. It gives a new point process that can serve as a network model exhibiting repulsion. The properties and functions of the new point process, such as moment measures, the Laplace functional, the void probabilities, as well as conditional (Palm) characteristics can be estimated accurately by simulating the underlying (non-thinned) point process, which can be taken, for example, to be Poisson. This is in contrast (and preference to) finite Gibbs point processes, which, instead of thinning, require weighting the Poisson realizations, involving usually intractable normalizing constants. Models based on determinantal point processes are also well suited for statistical (supervised) learning techniques, allowing the models to be fitted to observed network patterns with some particular geometric properties. We illustrate this approach by imitating with determinantal thinning the well-known Mat{é}rn~II hard-core thinning, as well as a soft-core thinning depending on nearest-neighbour triangles. These two examples demonstrate how the proposed approach can lead to new, statistically optimized, probabilistic transmission scheduling schemes.