  In this paper we introduce a new class of multivariate unimodal
distributions, motivated by Khintchine's representation. We start by proposing
a univariate model, whose support covers all the unimodal distributions on the
real line. The proposed class of unimodal distributions can be naturally
extended to higher dimensions, by using the multivariate Gaussian copula. Under
both univariate and multivariate settings, we provide MCMC algorithms to
perform inference about the model parameters and predictive densities. The
methodology is illustrated with univariate and bivariate examples, and with
variables taken from a real data-set.
