  Stein's method provides a way of bounding the distance of a probability
distribution to a target distribution $\mu$. Here we develop Stein's method for
the class of discrete Gibbs measures with a density $e^V$, where $V$ is the
energy function. Using size bias couplings, we treat an example of Gibbs
convergence for strongly correlated random variables due to Chayes and Klein
[Helv. Phys. Acta 67 (1994) 30--42]. We obtain estimates of the approximation
to a grand-canonical Gibbs ensemble. As side results, we slightly improve on
the Barbour, Holst and Janson [Poisson Approximation (1992)] bounds for Poisson
approximation to the sum of independent indicators, and in the case of the
geometric distribution we derive better nonuniform Stein bounds than Brown and
Xia [Ann. Probab. 29 (2001) 1373--1403].
