Our object of study is the general class of stick-breaking processes with exchangeable length variables. These generalize well-known Bayesian non-parametric priors in an unexplored direction. We give conditions to assure the respective species sampling process is proper and the corresponding prior has full support. For a rich sub-class we explain how, by tuning a single $[0,1]$-valued parameter, the stochastic ordering of the weights can be modulated, and Dirichlet and Geometric priors can be recovered. A general formula for the distribution of the latent allocation variables is derived and an MCMC algorithm is proposed for density estimation purposes.

翻译：我们的研究目标是具有可互换长度变量的固定破碎过程的一般类别。 这些将众所周知的贝耶斯非参数前科以一个未探索的方向加以概括化。 我们给各个物种取样过程提供条件,以确保相应的物种取样过程正确,相应的前科得到充分支持。 对于一个富裕的子类,我们解释如何通过调整单一的 $[10,1]美元价值的参数,调整重量的随机顺序,并可以回收迪里切特和几何前科。 得出了一个潜在分配变量分布的一般公式,并提出了用于密度估计的MCMC算法。