A Nonparametric Bayesian Solution of the Empirical Stochastic Inverse Problem
Haiyi Shi, Lei Yang, Jiarui Chi, Troy Butler, Haonan Wang, Derek Bingham, Don Estep
Published: 2025/9/26
Abstract
The stochastic inverse problem is a key ingredient in making inferences, predictions, and decisions for complex science and engineering systems. We formulate and analyze a nonparametric Bayesian solution for the stochastic inverse problem. Key properties of the solution are proved and the convergence and error of a computational solution obtained by random sampling is analyzed. Several applications illustrate the results.