论文标题
贝叶斯相关矢量机的后验一致性
Posterior Consistency for Bayesian Relevance Vector Machines
论文作者
论文摘要
统计建模和样本量的推理问题大大小于可用协变量的数量。 Chakraborty等。 （2012年）在这种情况下，使用基于复制核Hilbert Space（RKHS）的相关矢量机对非线性回归进行了完整的贝叶斯分析。但是他们没有提供与程序相关的任何理论属性。本文重新讨论了他们的问题，引入了一类新的全球局部先验，并提供后验一致性以及后验收率的结果
Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. Chakraborty et al. (2012) did a full hierarchical Bayesian analysis of nonlinear regression in such situations using relevance vector machines based on reproducing kernel Hilbert space (RKHS). But they did not provide any theoretical properties associated with their procedure. The present paper revisits their problem, introduces a new class of global-local priors different from theirs, and provides results on posterior consistency as well as posterior contraction rates
