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：在多保真度框架下，数据来源于高保真度和低保真度两种渠道。低保真度数据规模更大，可用于对高保真度变量的感兴趣量（例如均值）进行更高效的统计推断。本研究针对为高保真度数据更高效地拟合参数模型的目标，探讨了此类多保真度场景。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。同时提供了应用案例，涉及通过两种不同保真度的计算机代码生成极端船舶运动的量化分析。