Data from: Monte Carlo Strategies for selecting parameter values in simulation experiments

Simulation experiments are used widely throughout evolutionary biology and bioinformatics to compare models, promote methods, and test hypotheses. The biggest practical constraint on simulation experiments is the computational demand, particularly as the number of parameters increases. Given the extraordinary success of Monte Carlo methods for conducting inference in phylogenetics, and indeed throughout the sciences, we investigate ways in which Monte Carlo framework can be used to carry out simulation experiments more efficiently. The key idea is to sample parameter values for the experiments, rather than iterate through them exhaustively. Exhaustive analyses become completely infeasible when the number of parameters gets too large, whereas sampled approaches can fare better in higher dimensions. We illustrate the framework with applications to phylogenetics and genetic archaeology.进化生物学与生物信息学领域广泛应用仿真实验，用于模型比较、方法优化与假设检验。仿真实验面临的最大实际限制为计算资源需求，尤其当参数数量增多时更为显著。鉴于蒙特卡洛（Monte Carlo）方法在系统发育学（phylogenetics）乃至整个科学界的推断任务中取得了卓越成效，我们探索了利用蒙特卡洛框架更高效开展仿真实验的途径。其核心思路为对实验所需的参数值进行采样，而非穷举遍历所有参数组合。当参数数量过大时，穷举分析将完全不可行，而采样方法在高维参数空间中则表现更优。我们通过系统发育学与遗传考古学（genetic archaeology）中的应用案例对该框架进行了演示说明。