  B\'ezier simplex fitting algorithms have been recently proposed to
approximate the Pareto set/front of multi-objective continuous optimization
problems. These new methods have shown to be successful at approximating
various shapes of Pareto sets/fronts when sample points exactly lie on the
Pareto set/front. However, if the sample points scatter away from the Pareto
set/front, those methods often likely suffer from over-fitting. To overcome
this issue, in this paper, we extend the B\'ezier simplex model to a
probabilistic one and propose a new learning algorithm of it, which falls into
the framework of approximate Bayesian computation (ABC) based on the
Wasserstein distance. We also study the convergence property of the Wasserstein
ABC algorithm. An extensive experimental evaluation on publicly available
problem instances shows that the new algorithm converges on a finite sample.
Moreover, it outperforms the deterministic fitting methods on noisy instances.
