On the performance evaluation of a hierarchical-structure prototype product using inconsistent prior information and limited test data

Abstract

Performance evaluation or parameter estimation for a hierarchical structure prototype product is an imperative yet difficult task. The challenge comes from both inaccurate prior knowledge and insufficient data acquisition at its early usage period. In this paper, we propose an innovative Bayesian inference framework for the uncertainty quantification of the performance of a newly developed product with hierarchical structure characteristics. We focus particularly on the presence of inconsistent prior distributions on one quantity. An adaptive Bayesian melding method is investigated to aggregate multi-source information in a flexible way. We realize this method via a modified sampling importance re-sampling algorithm. This algorithm is designed to substitute for the MCMC methods and is computationally efficient. We use a practical engineering case to validate our proposal.