  Motivated by de Finetti's representation theorem for almost exchangeable
arrays, we want to sample $\mathbf p \in [0,1]^d$ from a distribution with
density proportional to $\exp(-A^2\sum_{i<j}c_{ij}(p_i-p_j)^2)$, where $A$ is
large and $c_{ij}$'s are non-negative weights. We analyze the rate of
convergence of a coordinate Gibbs sampler used to simulate from these measures.
We show that for every non-zero fixed matrix $C=(c_{ij})$, and large enough
$A$, mixing happens in $\Theta(A^2)$ steps in a suitable Wasserstein distance.
The upper and lower bounds are explicit and depend on the matrix $C$ through
few relevant spectral parameters.
