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与生物信息学领域应用广泛，常用于模型比对、方法优化与假说验证。模拟实验面临的最大实际瓶颈为计算资源需求，随着参数数量增长，该问题尤为突出。鉴于蒙特卡洛（Monte Carlo）方法在系统发育学乃至全学科的推断任务中已取得卓越成效，本研究探讨了如何借助蒙特卡洛框架更高效地开展模拟实验。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，而基于采样的方法在高维参数空间中则表现更优。本研究以系统发育学与遗传考古学中的应用为例，对该框架进行了演示说明。