Bayesian Calibration of Computer Modelsстатья из журнала
Аннотация: Summary We consider prediction and uncertainty analysis for systems which are approximated using complex mathematical models. Such models, implemented as computer codes, are often generic in the sense that by a suitable choice of some of the model's input parameters the code can be used to predict the behaviour of the system in a variety of specific applications. However, in any specific application the values of necessary parameters may be unknown. In this case, physical observations of the system in the specific context are used to learn about the unknown parameters. The process of fitting the model to the observed data by adjusting the parameters is known as calibration. Calibration is typically effected by ad hoc fitting, and after calibration the model is used, with the fitted input values, to predict the future behaviour of the system. We present a Bayesian calibration technique which improves on this traditional approach in two respects. First, the predictions allow for all sources of uncertainty, including the remaining uncertainty over the fitted parameters. Second, they attempt to correct for any inadequacy of the model which is revealed by a discrepancy between the observed data and the model predictions from even the best-fitting parameter values. The method is illustrated by using data from a nuclear radiation release at Tomsk, and from a more complex simulated nuclear accident exercise.
Год издания: 2001
Авторы: Marc C. Kennedy, Anthony O’Hagan
Издательство: Oxford University Press
Источник: Journal of the Royal Statistical Society Series B (Statistical Methodology)
Ключевые слова: Probabilistic and Robust Engineering Design, Fault Detection and Control Systems, Gaussian Processes and Bayesian Inference
Открытый доступ: bronze
Том: 63
Выпуск: 3
Страницы: 425–464