论文标题
部分可观测时空混沌系统的无模型预测
On Data Augmentation for Models Involving Reciprocal Gamma Functions
论文作者
论文摘要
在本文中，当相互伽马函数出现在完全条件密度的情况下时，我们将一种新的有效的数据增强方法引入了具有形状参数的模型的后验推断。我们的方法是通过使用高斯的乘法公式和stirling的公式来近似形状参数的完整条件密度，其中可以任意使近似误差任意地使近似误差。我们使用这些技术来构建有效的吉布斯和大都市 - 悬挂算法，以涉及涉及伽马分布，学生的$ t $分布，dirichlet分布，负二项式分布和WishArt分布的各种模型。通过模拟研究来证明所提出的采样方法。
In this paper, we introduce a new and efficient data augmentation approach to the posterior inference of the models with shape parameters when the reciprocal gamma function appears in full conditional densities. Our approach is to approximate full conditional densities of shape parameters by using Gauss's multiplication formula and Stirling's formula for the gamma function, where the approximation error can be made arbitrarily small. We use the techniques to construct efficient Gibbs and Metropolis-Hastings algorithms for a variety of models that involve the gamma distribution, Student's $t$-distribution, the Dirichlet distribution, the negative binomial distribution, and the Wishart distribution. The proposed sampling method is numerically demonstrated through simulation studies.
