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.

翻译：贝叶斯证据是模型选择的关键要素，可以说是在贝叶斯数据分析中最重要的量：然而，它同时也是最难计算的量之一。本文提出了一种分层方法，该方法利用后验概率密度的多维归一化近似，通过任意采样方案抽取的一组后验样本，以分层方式推断模型的证据。