Normal approximation for Gibbs processes via disagreement couplings

This work improves the existing central limit theorems (CLTs) for geometric functionals of Gibbs processes in three aspects. First, we derive a CLT for weakly stabilizing functionals, thereby improving on the previously used assumption of exponential stabilization. Second, we show that this CLT holds for interaction ranges up to the percolation threshold of the dominating Poisson process. This avoids imprecise branching bounds from graphical construction. Third, by constructing simultaneous couplings of several Palm processes for Gibbs functionals, we provide a quantitative CLT in terms of Kolmogorov bounds for normal approximation. An important conceptual ingredient in these advances is the extension of disagreement coupling adapted to increasing windows and to the comparison at multiple spatial locations.

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Publication Details

Journal
Electronic Journal of Probability
Published
2025-01-01
DOI
https://doi.org/10.1214/25-ejp1351
Primary Topic
Markov Chains and Monte Carlo Methods
Type
article
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article

Normal approximation for Gibbs processes via disagreement couplings

Anne Marie Svane, Moritz Otto, Christian Hirsch
Electronic Journal of Probability
Markov Chains and Monte Carlo Methods
article

Normal approximation for Gibbs processes via disagreement couplings

Anne Marie Svane, Moritz Otto, Christian Hirsch
article en

Abstract

This work improves the existing central limit theorems (CLTs) for geometric functionals of Gibbs processes in three aspects. First, we derive a CLT for weakly stabilizing functionals, thereby improving on the previously used assumption of exponential stabilization. Second, we show that this CLT holds for interaction ranges up to the percolation threshold of the dominating Poisson process. This avoids imprecise branching bounds from graphical construction. Third, by constructing simultaneous couplings of several Palm processes for Gibbs functionals, we provide a quantitative CLT in terms of Kolmogorov bounds for normal approximation. An important conceptual ingredient in these advances is the extension of disagreement coupling adapted to increasing windows and to the comparison at multiple spatial locations.

Electronic Journal of ProbabilityVol. 30(none)
Leiden University (NL), Aarhus University (DK), Mathematical Institute of the Slovak Academy of Sciences (SK), Aalborg University (DK)
Reduced inequalities
Openalex Percentile: Top 89%
Markov Chains and Monte Carlo Methods
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Normal approximation for Gibbs processes via disagreement couplings — Anne Marie Svane, Moritz Otto, et al. · Electronic Journal of Probability (2025) | TGRS Research Map | TGRS