In the context of computer models, calibration is the process of estimating unknown simulator parameters from observational data. Calibration is variously referred to as model fitting, parameter estimation/inference, an inverse problem, and model tuning. The need for calibration occurs in most areas of science and engineering, and has been used to estimate hard to measure parameters in climate, cardiology, drug therapy response, hydrology, and many other disciplines. Although the statistical method used for calibration can vary substantially, the underlying approach is essentially the same and can be considered abstractly. In this survey, we review the decisions that need to be taken when calibrating a model, and discuss a range of computational methods that can be used to compute Bayesian posterior distributions.

翻译：在计算机模型背景下，校准是指根据观测数据估计未知模拟器参数的过程。校准在不同领域分别被称为模型拟合、参数估计/推断、反问题求解及模型调优。科学和工程的大多数领域都需要校准，并已被用于估计气候学、心脏病学、药物疗效反应、水文学等众多学科中难以测量的参数。虽然用于校准的统计方法可能差异显著，但其核心方法本质相同，且可进行抽象处理。本综述回顾了校准模型时需要做出的决策，并讨论了一系列可用于计算贝叶斯后验分布的数值方法。