We consider an array of random variables, taking values in a complete and separable metric space, that exhibits a kind of symmetry which we call row exchangeability. Given such an array, a natural model for Bayesian nonparametric inference is the nested Dirichlet process (NDP). Exactly determining posterior distributions for the NDP is infeasible, since the computations involved grow exponentially with the sample size. In this paper, we present a new approach to determining these posterior distributions that involves the use of sequential

翻译：我们考虑一个取值于完备可分度量空间的随机变量阵列，该阵列呈现出一种我们称之为行可交换性的对称性。给定此类阵列，嵌套Dirichlet过程（NDP）可作为贝叶斯非参数推断的自然模型。精确确定NDP的后验分布是不可行的，因为相关计算量随样本量呈指数级增长。本文提出一种确定这些后验分布的新方法，该方法涉及序贯