  The concentration of measure phenomenon may be summarized as follows: a
function of many weakly dependent random variables that is not too sensitive to
any of its individual arguments will tend to take values very close to its
expectation. This phenomenon is most completely understood when the arguments
are mutually independent random variables, and there exist several powerful
complementary methods for proving concentration inequalities, such as the
martingale method, the entropy method, and the method of transportation
inequalities. The setting of dependent arguments is much less well understood.
This chapter focuses on the martingale method for deriving concentration
inequalities without independence assumptions. In particular, we use the
machinery of so-called Wasserstein matrices to show that the Azuma-Hoeffding
concentration inequality for martingales with almost surely bounded
differences, when applied in a sufficiently abstract setting, is powerful
enough to recover and sharpen several known concentration results for
nonproduct measures. Wasserstein matrices provide a natural formalism for
capturing the interplay between the metric and the probabilistic structures,
which is fundamental to the concentration phenomenon.
