  This paper presents a self-contained account for coupling arguments and
applications in the context of Markov processes. We first use coupling to
describe the transport problem, which leads to the concepts of optimal coupling
and probability distance (or transportation-cost), then introduce applications
of coupling to the study of ergodicity, Liouville theorem, convergence rate,
gradient estimate, and Harnack inequality for Markov processes.
