  The Dobrushin comparison theorem is a powerful tool to bound the difference
between the marginals of high-dimensional probability distributions in terms of
their local specifications. Originally introduced to prove uniqueness and decay
of correlations of Gibbs measures, it has been widely used in statistical
mechanics as well as in the analysis of algorithms on random fields and
interacting Markov chains. However, the classical comparison theorem requires
validity of the Dobrushin uniqueness criterion, essentially restricting its
applicability in most models to a small subset of the natural parameter space.
In this paper we develop generalized Dobrushin comparison theorems in terms of
influences between blocks of sites, in the spirit of Dobrushin-Shlosman and
Weitz, that substantially extend the range of applicability of the classical
comparison theorem. Our proofs are based on the analysis of an associated
family of Markov chains. We develop in detail an application of our main
results to the analysis of sequential Monte Carlo algorithms for filtering in
high dimension.
