A simple estimator of the correlation kernel matrix of a determinantal point process
Machine Learning
2025-05-21 v1 Machine Learning
Abstract
The Determinantal Point Process (DPP) is a parameterized model for multivariate binary variables, characterized by a correlation kernel matrix. This paper proposes a closed form estimator of this kernel, which is particularly easy to implement and can also be used as a starting value of learning algorithms for maximum likelihood estimation. We prove the consistency and asymptotic normality of our estimator, as well as its large deviation properties.
Keywords
Cite
@article{arxiv.2505.14529,
title = {A simple estimator of the correlation kernel matrix of a determinantal point process},
author = {Christian Gouriéroux and Yang Lu},
journal= {arXiv preprint arXiv:2505.14529},
year = {2025}
}