A Nonparametric Bayesian Solution of the Empirical Stochastic Inverse Problem
By: Haiyi Shi , Lei Yang , Jiarui Chi and more
Potential Business Impact:
Helps predict how things will work.
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.
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