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

翻译：Chakraborty等人(2012年)利用基于复制核心Hilbert空间(RKHHS)的相关矢量机器,对此类情况下的非线性回归进行了全面的巴伊西亚级分析。但是,它们没有提供与其程序相关的任何理论属性。本文件回顾了它们的问题,提出了与它们不同的新一类全球-地方前科,并提供了关于后端一致性和后端收缩率的结果。