Stein's method for positively associated random variables with applications to the Ising and voter models, bond percolation, and contact process
arXiv:1603.05322 · doi:10.1214/16-AIHP808
Abstract
We provide non-asymptotic bounds to the normal for four well-known models in statistical physics and particle systems in ; the ferromagnetic nearest-neighbor Ising model, the supercritical bond percolation model, the voter model and the contact process. In the Ising model, we obtain an distance bound between the total magnetization and the normal distribution at any temperature when the magnetic moment parameter is nonzero, and when the inverse temperature is below critical and the magnetic moment parameter is zero. In the percolation model we obtain such a bound for the total number of points in a finite region belonging to an infinite cluster in dimensions , in the voter model for the occupation time of the origin in dimensions , and for finite time integrals of non-constant increasing cylindrical functions evaluated on the one dimensional supercritical contact process started in its unique invariant distribution. The tool developed for these purposes is a version of Stein's method adapted to positively associated random variables. In one dimension, letting be a positively associated mean zero random vector with components that obey the bound , and whose sum has variance 1, it holds that where has the standard normal distribution and is the metric. Our methods apply in the multidimensional case with the metric replaced by a smooth function metric.
43 pages
References in corpus (4)
- A new proof of the sharpness of the phase transition for Bernoulli percolation and the Ising model
- Stein's method for discrete Gibbs measures
- Stein's method for dependent random variables occurring in Statistical Mechanics
- The truncated correlations of the Ising model in any dimension decay exponentially fast at all but the critical temperature
Cited by in corpus (3)
- Stein's method for negatively associated random variables with applications to second order stationary random fields
- Normal approximation for associated point processes via Stein's method with applications to determinantal point processes
- Biasing with an independent increment: Gaussian approximations and proximity of Poisson mixtures