Abstract:
The notion of a successful coupling of Markov processes, based on the idea that
both components of a coupled system “intersect” in finite time with probability 1, is extended
to cover situations where the coupling is not necessarily Markovian and its components only
converge (in a certain sense) to each other with time. Under these assumptions the unique
ergodicity of the original Markov process is proved. The price for this generalization is the
weak convergence to the unique invariant measure instead of the strong convergence. Applying
these ideas to infinite interacting particle systems, we consider even more involved situations
where the unique ergodicity can be proved only for a restriction of the original system to a
certain class of initial distributions (e.g., translation-invariant). Questions about the existence
of invariant measures with a given particle density are also discussed.