Filtered-Variate Prior Distributions for Histogram Smoothing
本文提出一种先验分布，用于推断平滑的总体频率（即相邻类别概率差异小的概率向量），通过线性变换（滤波）构造随机概率向量，并给出基于典型平滑概率向量列表的先验评估方法。
We develop prior distributions for histogram inference favoring smooth population frequencies; that is, probability vectors with small differences for neighboring categories. We give a theory of prior-random probability vectors representable as a linear transform, or "filter," of a standard random probability vector, or equivalently, a random weighted average of nonrandom smooth probability vectors. Promising methods of prior assessment are given based on elicitation of a list of typically smooth probability vectors, the empirical moments of which can then be matched by the mean vector and variance matrix of a constructed continuous-type filtered-variate prior distribution.