Discrete random probability measures are a key ingredient of Bayesian nonparametric inferential procedures. A sample generates ties with positive probability and a fundamental object of both theoretical and applied interest is the corresponding random number of distinct values. The growth rate can be determined from the rate of decay of the small frequencies implying that, when the decreasingly ordered frequencies admit a tractable form, the asymptotics of the number of distinct values can be conveniently assessed. We focus on the geometric stick-breaking process and we investigate the effect of the choice of the distribution for the success probability on the asymptotic behavior of the number of distinct values. We show that a whole range of logarithmic behaviors are obtained by appropriately tuning the prior. We also derive a two-term expansion and illustrate its use in a comparison with a larger family of discrete random probability measures having an additional parameter given by the scale of the negative binomial distribution.
翻译:分解随机概率测量是巴伊西亚非参数推论程序的一个关键要素。 样本产生正概率, 理论和应用兴趣的基本对象就是不同值的相应随机数。 增长率可以从小频率的衰减速度中确定, 意指, 当订购量减少的频率接受一种可移动的形式时, 可以方便地评估不同值数的无症状。 我们侧重于几何粘断过程, 并调查不同值数的成功概率分布选择对无症状概率的影响。 我们显示, 通过适当调整前一数值可以取得一系列的对数行为 。 我们还得出了两期扩展, 并演示其使用, 与较大系列的离散随机概率测量方法相比较, 后者具有由负二进制分布尺度给出的额外参数 。