Abstract
Decreasing weight prior distributions for mixture models play an important role in nonparametric Bayesian inference. Various random probability measures with decreasing weights have been previously explored and it has been shown that they provide an efficient alternative to the more traditional Dirichlet process mixture model. This ordering of the weights implicitly alleviates the so-called label switching problem, as larger weights correspond to larger groups. A general procedure to define any decreasing weights model based on a characterization of a discrete random variable which also allows for an easy and generic sampling algorithm for estimating the model is provided. An exact representation for the number of expected components is given. Finally, the performance of the mixture model on simulated data sets is investigated numerically.
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