In this talk, we provide a rigorous theoretical framework for studying the problem of approximating heavy-tailed distributions via ergodic SDEs driven by symmetric stable processes. Motivated by recent works on the use of heavy tailed processes in Markov Chain Monte Carlo, we show that chains driven by the stable noise can have better contraction rates than corresponding chains driven by the Gaussian noise, due to the heavy tails of the stable distribution.