  The Bayesian evidence, crucial ingredient for model selection, is arguably
the most important quantity in Bayesian data analysis: at the same time,
however, it is also one of the most difficult to compute. In this paper we
present a hierarchical method that leverages on a multivariate normalised
approximant for the posterior probability density to infer the evidence for a
model in a hierarchical fashion using a set of posterior samples drawn using an
arbitrary sampling scheme.
