AI 中文总结
该研究针对狄利克雷过程作为采样分布的特性及参数推断空白，将其作为数据生成模型，对中心测度和精度参数做贝叶斯推断，结合模拟与真实数据集验证方法。
AI 中文摘要
狄利克雷过程（DP）是最常见的贝叶斯非参数先验，然而其作为采样分布的特性及其参数的推断尚未被研究。本文将DP用作数据生成模型，对其中心测度和精度参数进行贝叶斯推断，以直方图序列作为观测数据示例，考虑了模拟数据集和真实数据集。
英文摘要
The Dirichlet process (DP) is the most common bayesian nonparametric prior, however, its properties as sampling distribution have not been studied nor inference on its parameters. Here we use the DP as a data generating model and make bayesian inference on its centering measure and precision parameter. We illustrate with a sequence of histograms as observed data. In particular, we consider simulated and real datasets.
