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.模拟实验在进化生物学（evolutionary biology）与生物信息学（bioinformatics）领域应用广泛，可用于模型对比、方法推广与假设验证。模拟实验面临的最大实际限制为计算资源开销，尤其当参数数量增加时。鉴于蒙特卡洛方法（Monte Carlo Methods）在系统发育学（phylogenetics）乃至整个科学领域开展统计推断任务时所取得的卓越成就，本文探究了借助蒙特卡洛框架更高效地开展模拟实验的路径。其核心思路是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。当参数规模过大时，穷举分析将完全不可实现，而采样方法在高维参数空间中则能取得更优表现。我们通过系统发育学与遗传考古学（genetic archaeology）领域的应用案例，对该框架进行了演示说明。