In a multi-fidelity setting, data are available from two sources, high- and low-fidelity. Low-fidelity data has larger size and can be leveraged to make more efficient inference about quantities of interest, e.g. the mean, for high-fidelity variables. In this work, such multi-fidelity setting is studied when the goal is to fit more efficiently a parametric model to high-fidelity data. Three multi-fidelity parameter estimation methods are considered, joint maximum likelihood, (multi-fidelity) moment estimation and (multi-fidelity) marginal maximum likelihood, and are illustrated on several parametric models, with the focus on parametric families used in extreme value analysis. An application is also provided concerning quantification of occurrences of extreme ship motions generated by two computer codes of varying fidelity.

翻译：在多保真度设定中，数据可从高保真度和低保真度两种来源获取。低保真度数据规模更大，可用于对高保真度变量的关注量（如均值）进行更高效的推断。本研究探讨了以更高效地将参数模型拟合至高保真度数据为目标的多保真度设定。我们考虑了三种多保真度参数估计方法：联合最大似然估计、（多保真度）矩估计以及（多保真度）边际最大似然估计，并在多个参数模型上进行了演示，重点关注极值分析中使用的参数族。同时提供了一个应用实例，涉及对两种不同保真度的计算机代码所生成的极端船舶运动发生频率的量化分析。