2023/11/01 by Sid Kankanala, Kankanala, Sid
Mathematics · Decision Sciences · Computer Science · #Statistical Methods and Inference #Advanced Statistical Process Monitoring #Gaussian Processes and Bayesian Inference
paper · pdf · doi:10.48550/arxiv.2311.00662
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.