Estimates on the coverage of parameter space using populations of models

In this paper we provide estimates for the coverage of parameter space when using Latin Hypercube Sampling, which forms the basis of building so-called populations of models. The estimates are obtained using combinatorial counting arguments to determine how many trials, k, are needed in order to obtain specified parameter space coverage for a given value of the discretisation size n. In the case of two dimensions, we show that if the ratio () of trials to discretisation size is greater than 1, then as n becomes moderately large the fractional coverage behaves as 1 e. We compare these estimates with simulation results obtained from an implementation of Latin Hypercube Sampling using MATLAB