  Non-Gaussian concentration estimates are obtained for invariant probability
measures of reversible Markov processes. We show that the functional
inequalities approach combined with a suitable Lyapunov condition allows us to
circumvent the classical Lipschitz assumption of the observables. Our method is
general and covers diffusions as well as pure-jump Markov processes on
unbounded spaces.
