An ABC algorithm for random partitions arising from the Dirichlet process

Dealing with latent random partitions might be a tedious task, due to mathematically and computationally tractability of the problem. We propose an approximate Bayesian computation (ABC) approach to deal with the estimation of random partitions latent in sets of exchangeable data. Furthermore, we present some preliminary simulation results of the novel proposal, investigating both the quality of the sample produced and the computational time required.