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.