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 that a 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 to 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与生物信息学领域应用广泛，常用于模型对比、方法研发与假说检验。模拟实验面临的最大实际限制为计算负荷，且该限制会随参数数量的增加而愈发突出。鉴于蒙特卡洛（Monte Carlo）方法在系统发育学（phylogenetics）乃至全学科的统计推断中已取得卓越成效，本文探究了借助蒙特卡洛框架更高效开展模拟实验的可行路径。其核心思路为对实验所需的参数值进行采样，而非穷尽式遍历所有参数组合。当参数规模过大时，穷尽式分析将完全不可行，而采样方法在高维参数空间中则具备更优表现。我们通过系统发育学与遗传考古学的应用实例，对该框架进行了演示说明。