基于条件Gibbs结构的贝叶斯非参数估计
Bayesian nonparametric estimators derived from conditional Gibbs structures
摘要：
我们考虑离散非参数先验诱导吉布斯型可交换随机分区，并详细研究其后验行为。特别是，我们推导出条件分布和相应的贝叶斯非参数估计，它可以很容易地利用预测的各种功能的额外的样本。这些结果为需要预测未来结果的基因组应用提供了有用的工具。
英文摘要：
We consider discrete nonparametric priors which induce Gibbs-type exchangeable random partitions and investigate their posterior behavior in detail. In particular, we deduce conditional distributions and the corresponding Bayesian nonparametric estimators, which can be readily exploited for predicting various features of additional samples. The results provide useful tools for genomic applications where prediction of future outcomes is required.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
