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:建模复杂的真实世界情境,如传染病、地质现象和生物过程,可能面临一个两难的问题:计算机模型(称为仿真器)需要足够复杂以捕捉系统的动态,但每增加一个复杂度,仿真的计算时间就会增加,使得获得与观察结果一致的参数选择描述变得困难。虽然已经存在了一些鉴定与现实观测数据相符的备选模型的方法,例如优化或马尔可夫链蒙特卡罗方法,但它们可能得出非稳健性的推断,而且对于计算密集型仿真器可能不可行。仿真和历史匹配技术使得这些决策变得可行,能够有效地识别出产生与数据相符的接受参数空间区域,同时为仿真器结构提供有价值的信息,但进行仿真所需的数学考虑可能会对仿真器的制作者和用户构成一定的障碍,与其他方法相比。hmer包为在仿真数据上使用历史匹配和仿真提供了一个易于访问的框架,利用了该方法的计算效率,同时使用户能够轻松匹配、可视化并从其复杂的仿真器中进行稳健预测。