标题： 关于条件矩限制模型中的高斯过程先验 显示英文标题

标题： On Gaussian Process Priors in Conditional Moment Restriction Models

摘要： 本文研究了非参数条件矩限制下未知函数的拟贝叶斯估计和不确定性量化。 我们推导了一类高斯过程先验的收缩速度。 此外，我们给出了拟后验分布满足伯恩斯坦-冯·米塞斯定理的条件。 因此，我们证明了最优加权拟贝叶斯可信集具有精确的渐近频率覆盖。 显示英文摘要

摘要： This paper studies quasi Bayesian estimation and uncertainty quantification for an unknown function that is identified by a nonparametric conditional moment restriction. We derive contraction rates for a class of Gaussian process priors. Furthermore, we provide conditions under which a Bernstein von Mises theorem holds for the quasi-posterior distribution. As a consequence, we show that optimally weighted quasi-Bayes credible sets have exact asymptotic frequentist coverage.