We obtain upper bounds for the total variation distance between the distributions of two Gibbs point processes in a very general setting. Applications are provided to various well-known processes and settings from spatial statistics and statistical physics, including the comparison of two Lennard-Jones processes, hard core approximation of an area interaction process and the approximation of lattice processes by a continuous Gibbs process. Our proof of the main results is based on Stein's method. We construct an explicit coupling between two spatial birth-death processes to obtain Stein factors, and employ the Georgii-Nguyen-Zessin equation for the total bound
The object of this thesis is the study of some analytical and asymptotic properties of Markov proces...
The object of this thesis is the study of some analytical and asymptotic properties of Markov proces...
The object of this thesis is the study of some analytical and asymptotic properties of Markov proces...
summary:In the paper asymptotic properties of functionals of stationary Gibbs particle processes are...
summary:In the paper asymptotic properties of functionals of stationary Gibbs particle processes are...
summary:In the paper asymptotic properties of functionals of stationary Gibbs particle processes are...
Stein's method provides a way of bounding the distance of a probability distribution to a target dis...
In this article, superpositions of possibly dependent point processes on a general space are conside...
peer reviewedWe build on the formalism developed in Ernst et al. (First order covariance inequalitie...
We derive explicit lower and upper bounds for the probability generating functional of a stationary ...
In this paper, we apply the Stein's method in the context of point processes, namely when the target...
We extend the ideas of Barbour's paper from 1990 and adapt Stein's method for distributional approxi...
AbstractIn this article, superpositions of possibly dependent point processes on a general space X a...
The Stein's method is a collection of probabilistic techniques for answering the ques- tion as to ho...
The object of this thesis is the study of some analytical and asymptotic properties of Markov proces...
The object of this thesis is the study of some analytical and asymptotic properties of Markov proces...
The object of this thesis is the study of some analytical and asymptotic properties of Markov proces...
The object of this thesis is the study of some analytical and asymptotic properties of Markov proces...
summary:In the paper asymptotic properties of functionals of stationary Gibbs particle processes are...
summary:In the paper asymptotic properties of functionals of stationary Gibbs particle processes are...
summary:In the paper asymptotic properties of functionals of stationary Gibbs particle processes are...
Stein's method provides a way of bounding the distance of a probability distribution to a target dis...
In this article, superpositions of possibly dependent point processes on a general space are conside...
peer reviewedWe build on the formalism developed in Ernst et al. (First order covariance inequalitie...
We derive explicit lower and upper bounds for the probability generating functional of a stationary ...
In this paper, we apply the Stein's method in the context of point processes, namely when the target...
We extend the ideas of Barbour's paper from 1990 and adapt Stein's method for distributional approxi...
AbstractIn this article, superpositions of possibly dependent point processes on a general space X a...
The Stein's method is a collection of probabilistic techniques for answering the ques- tion as to ho...
The object of this thesis is the study of some analytical and asymptotic properties of Markov proces...
The object of this thesis is the study of some analytical and asymptotic properties of Markov proces...
The object of this thesis is the study of some analytical and asymptotic properties of Markov proces...
The object of this thesis is the study of some analytical and asymptotic properties of Markov proces...