Combining pre- and post-model information in the uncertainty quantification of non-deterministic models using an extended Bayesian melding approach

Due to the increasing complexity of manufacturing process and the diversity of information sources, it is not rare in practical engineering that multiple priors are simultaneously available on the same quantity. To address this issue, which occurs due to inconsistent information from different sources, we propose a probability framework to quantify the uncertainty of a general propagation model. An extended Bayesian melding approach is developed to eliminate the limitations inherent in traditional Bayesian methods. It is found that the aggregation error, which is caused by inconsistent information from multi-sources, can be alleviated by combining the pre- and post-model information. Novel features of our approach involve a modified sampling importance resampling algorithm in which a distribution mixture technique is adopted to reduce the computational cost. To meet practical engineering requirements, this approach is extended to a non-deterministic scenario that has not been covered by existing studies. We use several case studies to validate our proposal as well as its benefits in practical applications. (C) 2019 Elsevier Inc. All rights reserved.