Bayesian Inference for Stable Distributions
针对稳定分布概率密度函数不存在导致参数估计困难的问题，利用马尔可夫链蒙特卡洛方法进行贝叶斯推断，从参数分布中抽样。
Very little work on stable distribution parameter estimation and inference appears in the literature due to the nonexistence of the probability density function. This has led in particular to a dearth of Bayesian work in this area. But Bayesian computation via Markov chain Monte Carlo allows us to sample from the distribution of the parameters of the stable distributions, by exploiting a particular mathematical representation involving the stable density.