Modelling complex real-world situations such as infectious diseases, geological phenomena, and biological processes can present a dilemma: the computer model (referred to as a simulator) needs to be complex enough to capture the dynamics of the system, but each increase in complexity increases the evaluation time of such a simulation, making it difficult to obtain an informative description of parameter choices that would be consistent with observed reality. While methods for identifying acceptable matches to real-world observations exist, for example optimisation or Markov chain Monte Carlo methods, they may result in non-robust inferences or may be infeasible for computationally intensive simulators. The techniques of emulation and history matching can make such determinations feasible, efficiently identifying regions of parameter space that produce acceptable matches to data while also providing valuable information about the simulator's structure, but the mathematical considerations required to perform emulation can present a barrier for makers and users of such simulators compared to other methods. The hmer package provides an accessible framework for using history matching and emulation on simulator data, leveraging the computational efficiency of the approach while enabling users to easily match to, visualise, and robustly predict from their complex simulators.
翻译:模拟复杂的现实状况,如传染病、地质现象和生物过程等,可能产生一个两难境地:计算机模型(称为模拟器)需要足够复杂,足以捕捉系统的动态,但每增加一个复杂程度,都会增加模拟的评估时间,从而增加这种模拟的评估时间,从而难以获得符合观察现实的参数选择的信息性描述;虽然确定与现实观测相匹配的可接受的方法存在,例如优化或马可夫链蒙特卡洛方法等,但它们可能导致非紫色的推断,或对计算密集模拟器可能不可行。模拟和历史比对技术可以使这种确定变得可行,有效地查明产生可接受数据匹配的参数空间区域,同时提供关于模拟器结构的宝贵信息,但进行模拟所需的数学考虑可以为模拟器的制造者和使用者提供了障碍,比其他方法更方便地提供了使用历史匹配和模拟模拟数据的框架,同时使用户能够从复杂的预测中轻易地匹配、视觉和模拟器的计算效率。