论文标题
一些稀疏的贝叶斯学习模型的后部不当
Posterior Impropriety of some Sparse Bayesian Learning Models
论文作者
论文摘要
稀疏的贝叶斯学习模型通常用于与观测值相比，协变量数量明显大得多的数据集中的预测。这样的模型通常采用复制的内核希尔伯特空间（RKHS）来执行预测任务，并且可以使用适当或不当的先验来实施。在本文中，我们表明，在使用不正确的先验实施文献中的一些稀疏的贝叶斯学习模型会导致后代不当。
Sparse Bayesian learning models are typically used for prediction in datasets with significantly greater number of covariates than observations. Such models often take a reproducing kernel Hilbert space (RKHS) approach to carry out the task of prediction and can be implemented using either proper or improper priors. In this article we show that a few sparse Bayesian learning models in the literature, when implemented using improper priors, lead to improper posteriors.
