Work

Bayesian Modeling of Gibbs Point Processes via Basis Function Expansions

Year: 2026

Type: preprint

Abstract: We present a hierarchical Bayesian framework for non-homogeneous pairwise interaction Gibbs point process models, where the global and local effect functions are modeled via basis function expansions. We further propose a testing procedure in order to assess complete spatial randomness. The proposed methodology is exemplified through two real benchmark data examples involving water striders and forest fires.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2608.12510

Authors: Hassett, Christopher, Micheas, Athanasios C., Holan, Scott H., Dostoglou, Stamatis

Institution: University of Missouri

Topic: Point processes and geometric inequalities

Subfield: Applied Mathematics

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Award: Collaborative Research: Randomization Based Machine Learning Methods in a Bayesian Model Setting for Data From a Complex Survey or Census

OpenAlex ID: W7203460942

Cites:

Cited by:

Related to: