Limit theorems for geometric functionals of Gibbs point processes

Observations are made on a point process Ξ in R in a window Qλ of volume λ. The observation, or ‘score’ at a point x, here denoted ξ(x, Ξ), is a function of the points within a random distance of x. When the input Ξ is a Poisson or binomial point process, the large λ limit theory for the total score ∑ x∈Ξ∩Qλ ξ(x, Ξ ∩Qλ), when properly scaled and centered, is well understood. In this paper we establish general laws of law numbers, variance asymptotics, and central limit theorems for the total score for Gibbsian input Ξ. The proofs use perfect simulation of Gibbs point processes to establish their mixing properties. The general limit results are applied to random sequential packing and spatial birth growth models, Voronoi and other Euclidean graphs, percolation models, and quantization problems involving Gibbsian input.