On an Approximate Likelihood for Quantiles
当仅知道分位数的先验分布时，贝叶斯推断因似然函数不完整而困难。本文提出一种近似似然函数的方法，并证明它能提供保守推断。
Let Z1… Zn be a random sample from F, an uncertain one-dimensional distribution function, and suppose that a prior distribution is available only for θ, a vector of quantiles of F. Bayesian inference is difficult because the likelihood function is not fully specified. This paper considers a method of approximating the likelihood function and shows that it provides conservative inferences.