Bayesian supervised predictive classifiers, hypothesis testing, and parametric estimation under Partition Exchangeability are implemented. The two classifiers presented are the marginal classifier (that assumes test data is i.i.d.) next to a more computationally costly but accurate simultaneous classifier (that finds a labelling for the entire test dataset at once based on simultanous use of all the test data to predict each label). We also provide the Maximum Likelihood Estimation (MLE) of the only underlying parameter of the partition exchangeability generative model as well as hypothesis testing statistics for equality of this parameter with a single value, alternative, or multiple samples. We present functions to simulate the sequences from Ewens Sampling Formula as the realisation of the Poisson-Dirichlet distribution and their respective probabilities.
翻译:在可交换性分区下,实施了贝叶斯人监督的预测分类、假设测试和参数估计。所介绍的两个分类者是边际分类者(假设测试数据为i.d.),旁边是计算成本更高、但准确的同步分类者(根据模拟使用所有测试数据来预测每个标签,发现整个测试数据集的标签)。我们还提供了分配可交换性基因模型唯一基本参数的最大相似性估计值,以及假设测试统计数据,以使该参数具有单一值、替代值或多个样本的平等性。我们提出功能,模拟Ewens抽样公式的序列,作为Poisson-Drichlet分布及其各自概率的实现情况。